In the visual cortex, stimuli outside the classical receptive field (CRF) modulate the neural firing rate, without driving the neuron by themselves. In the primary visual cortex (V1), such contextual modulation can be parametrized with an area summation function (ASF): increasing stimulus size causes first an increase and then a decrease of firing rate before reaching an asymptote. Earlier work has reported increase of sparseness when CRF stimulation is extended to its surroundings. However, there has been no clear connection between the ASF and network efficiency. Here we aimed to investigate possible link between ASF and network efficiency. In this study, we simulated the responses of a biomimetic spiking neural network model of the visual cortex to a set of natural images. We varied the network parameters, and compared the V1 excitatory neuron spike responses to the corresponding responses predicted from earlier single neuron data from primate visual cortex. The network efficiency was quantified with firing rate , entropy per spike and population sparseness. All three measures together provided a clear association between the network efficiency and the ASF. The association was clear when varying the horizontal connectivity within V1, which influenced both the efficiency and the distance to ASF, DAS. Given the limitations of our biophysical model, this association is qualitative, but nevertheless suggests that an ASF-like receptive field structure can cause efficient population response. Contextual stimuli outside the classical receptive field (CRF) modulate the responses of visually responsive cortical neurons  with one unit of energy (as the constraint). An early theoretical study suggested that reduction of redundancy in correlated neural activation patterns will improve coding efficiency Barlow, . Later, Several studies have associated contextual modulation with efficient coding. In particular, Vinje and Gallant  showed tCavanaugh et al.  providedR(x) as a function of stimulus contrast and size with a relation of two Gaussian functions, Equation (1).x is the diameter of circular stimulus and  and  are the gain and spatial extents of the center and surround components' coefficients, respectively. The gain coefficients (kc and ks) are dependent on the stimulus overall contrast , were obtained from this data for each neuron, with a Nelder-Mead simplex optimization algorithm  for each neuron in the primary visual cortex. The response was dependent on the eccentricity of the neural receptive field center in the visual field.This model describes neural response to a grating stimulus, as a relation between excitatory and inhibitory Gaussian functions. The parameters for the Gaussian functions were estimated from the ASF data of the monkey cells , see Equation (1) and Figure The area summation response , and the other one with a smaller summation field and stronger suppression  compared to natural ASF.To control how the amount of suppression and size of the summation field is related to the network efficiency, we generated two artificial ASFs, in addition to the natural ASF. One with a larger summation field and no suppression  compared to full-resolution retina.We did two sets of control simulations. In the first set, we checked the effect of retina resolution on the simulations results. The density of midget cells was decreased to 1/5 of the original (see Section Methods). The decrease in density resulted in a lower number of input filters, and correspondingly in spatially larger filters, apparently shifting the filter sensitivity toward lower spatial frequencies. The results show almost similar or slightly better link between DAS and the efficiency measures [Figure p < 0.05) at Figure *).In the second set of control simulations, we checked the effect of compartmental neurons  on the simulations results. In this control, we replaced the compartmental neurons with pointwise neurons (see Section Methods). The results confirm that the compartments have a clear role in linking the area summation with the efficiency of the network . However, the smaller summation field and stronger suppression showed almost similar dependency between entropy per spike and DAS than natural ASF .Finally, we tested dependence of the relation between efficiency of the network output and ASF, and the properties of the ASF. We generated two artificial ASFs, to test how the amount of suppression and size of the summation field is related to the network efficiency. If the natural ASF is optimal for natural images processed by a biophysically meaningful network, we should observe loss of efficiency when changing the function form. Figure We studied the relationship between the ASF and the efficiency of neural population code, using a biomimetic simulation of the visual cortex. Our results show that the ASF is associated with reduced energy consumption, better information carrying capacity (as measured by the entropy per spike), and higher population sparseness. The relation was typically nonlinear, but consistently present. Consequently, we suggest that the nonlinear and nonmonotonic area summation property of visual neurons is related to energy efficiency in the visual system. The result was further refined in several control setups. Our simulator seemed to work better with low spatial frequency input than with the images containing both high and low frequencies suggesting need to refine the model in future studies. In addition, excluding the parameter combinations which led to saturated or low-responsive activation patterns improved the link between entropy per spike and ASF, suggesting that the association is best at the dynamic range of firing rates. Moreover, an artificial ASF with a monotonically increasing summation function was more poorly associated with the efficiency, compared to the natural ASF of macaque monkeys. This indicates that the surround suppression of the ASF is important for the efficiency.Our findings are in line with biological data, such as a previous contextual modulation study  is a measure of energy efficiency, but not coding efficiency, because it does not necessarily lead to better classification of the activation patterns according to their information content. A network that is efficient in energy consumption might have no useful information in the network to classify images. In our model network, the lack of information content is expected because in our neural network the receptive fields have no structure, just the position, and scatter. Future work will need to address the effect of receptive field structure  and meaningful information carried by the network on the model output distance to ASF.p = 0.65) relationship between ASF and the efficiency measures emerge with smaller summation field and stronger suppression. In addition, the result of Figure p < 0.001), which suggests that inhibition rather than summation field size is the important attribute of the ASF in maintaining efficacy of the system. Nevertheless, our model shows robust dependence of efficiency with the non-linear ASF, and the mismatch in the summation field size most likely creating a positive baseline to the total error measure. Moreover, our results show considerable variations in the absolute values of the efficiency measures for a specific DAS value which might emerge from un-physiological parameter ranges, non-linearities of the system, or the unavoidable simplifications in our model.Our model is a rough simplification of the primate visual system, and the reduction of complexity most likely modifies the results of this study. First of all we have a reduced number of units in our neural network model, compared to the primate visual cortex, with our resolution corresponding to ~0.1° at 5° eccentricity. This may directly impact the processing of the high-frequency content of the images. Further, the model implements only one higher visual area and ignores corticothalamic feedback. Second, our simulation of visual cortex lacks the layered structure of the primate cerebral cortex. Instead, we model all neural activation with one layer, which most likely results in important omissions of parameters affecting the results. Third, we only have spatial position tuning in our model and no orientation, speed, disparity, spatial frequency, ocular dominance or wavelength tuning, as primate V1 cells do. One discrepancy with primate cortex is apparent in Figure We run the simulation for 200 ms before the visual input to reach a stable baseline and thereafter presented stationary input for 300 ms. The mean firing rate was calculated based on the last 300 ms time interval.Primarily we used comparable density of midget cells to the human retina in our simulations. This results in a very high computational load, which can be suboptimal given the much lower sampling of the biomimetic cortex. A set of control simulations showed that 1/5 of this density of midget cells can lead to a somewhat better association between area summation and efficiency of the neural network Figure . The betClearly, the optimal size for the summation and suppression fields of the ASF depends on the input features. Each 20 gray-scale natural images drew a distinct path into the three diagrams of efficiency vs. DAS. The results from middle column of Figure For each natural image, we had 625 simulation runs, which came from varying the extrastriate connectivity and inhibition within V1 as free parameters. In the simulations, the number of lateral connection between V1 excitatory and inhibitory cells seems to be a more determinative parameter to match the target ASF compared to the feedforward-feedback connections between V1 and the extrastriate area Figure . ClearlyThe ASF can be regarded as a comprehensive model of the spatial receptive field, including the summation close to the center of the receptive field (CRF) as well as contextual positive (summation field) and negative (surround suppression) modulation of a neuron's response. In the current study, we were interested in the relation of the ASF and the efficiency of neural output at the system level. Apparently, the nonlinear ASF helps to avoid saturations of the response patterns. The largest spike counts were associated with large distances from the predicted ASF target. In addition, the middle 70% of the dynamic range of the firing rate showed strong association between entropy per spike and ASF. While the relation between sparseness and the ASF target was not so straightforward within this same dynamic range and indeed appeared to be positively correlated, high sparseness were overall well associated with ASF. Moreover, for the low-resolution input, sparseness had an overall better association with the ASF.In summary, our study shows that the nonlinear ASF, which is a mathematical formulation of extra-CRF modulation onto the CRF responses, is related to efficiency of a neuronal population model.FS designed, simulated, and analyzed the neural network and wrote the paper. HH designed the neural network, and participated in writing the paper. RV participated in data analysis and writing the paper. SV designed and analyzed the neural network, and wrote the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Asset Management is an important component of an infrastructure project. A significant cost is involved in maintaining and updating the asset information. Data collection is the most time-consuming task in the development of an asset management system. In order to reduce the time and cost involved in data collection, this paper proposes a low cost Mobile Mapping System using an equipped laser scanner and cameras. First, the feasibility of low cost sensors for 3D asset inventory is discussed by deriving appropriate sensor models. Then, through calibration procedures, respective alignments of the laser scanner, cameras, Inertial Measurement Unit and GPS  antenna are determined. The efficiency of this Mobile Mapping System is experimented by mounting it on a truck and golf cart. By using derived sensor models, geo-referenced images and 3D point clouds are derived. After validating the quality of the derived data, the paper provides a framework to extract road assets both automatically and manually using techniques implementing RANSAC plane fitting and edge extraction algorithms. Then the scope of such extraction techniques along with a sample GIS (Geographic Information System) database structure for unified 3D asset inventory are discussed. Mobile mapping system tends to be more advantageous in these scenarios. More recent mapping products with up-to date data is preferred over highly accurate outdated data for most cartography applications. Since the field techniques are largely subjective, it calls for a technology that is both economical and expedient. With the introduction of low cost and portable laser scanners in the market, most of the mobile mapping systems are equipped with laser scanners in addition to video cameras. Thus, a combination of georeferenced pictures and three-dimensional point cloud provides a better scope for extracting information from the scene.The lifetime of Civil Engineering infrastructure projects are generally made up of three phases—design and planning, construction and maintenance. The use and maintenance phase has the longest duration and involves most of the project cost. Different construction engineering projects, such as development of roads, bridges, parks and other utilities are expected to be in a usable state for several years. However, damage and deterioration are unavoidable. The amount of money spent on repair or replacement of the assets are also very high. In order to make the process of maintenance and improvements more manageable, it is necessary to document the assets and inventories in an appropriate format. The recent advancements in digital maps and GIS (Geographic Information System) technology has improved the efficiency of asset and inventory management. GPS  is an instrument that is widely used to locate and map the positions of assets. The most important task in the development of an asset management system is the collection of accurate spatial data and its related attributes. Manual data collection using field data loggers with GPS and traditional survey methods are commonly seen for asset inventory data collection. Though this methodology provides accurate results, it involves more time and manpower. Several remote sensing products, such as aerial imageries, terrestrial photographs and laser point clouds with high accuracies also contribute to the development of an asset inventory database. This paper focuses on development and usage of a mobile mapping system for documenting transportation asset inventories. A typical mobile mapping system is equipped with a laser scanner, panoramic cameras, GPS and IMU  positioning setup, on-board computer and a storage device for data logging. Airborne LiDAR data and aerial images are highly suitable for extracting features, such as building footprints, roads and vegetation cover. Mapping of infrastructure facilities may require much higher level of detail including accurate positions of road signs and signals, curb height and width of pavements, availability of ramps on the sidewalks, The paper is divided into following sections: The first section discusses the background for the study which includes a detailed description about existing methods of data collection and literature references supporting various advancements in applications of mobile mapping systems in the domain of asset management. The second section describes the methodology involved which includes the task of building the system, different components and their communication networks, co-registering multi-sensor data, basic processing of the data and integrating multi-sensor data into a single GIS system. Specific details about the experiments conducted, discussion about the proposed framework and concluding remarks.etc. [Spatial data about roadside asset inventories and other municipal utilities are generally collected using traditional land survey methods. There are two types of information recorded—inventory (structures and road signs) and condition of the inventories . The fieetc. . Though etc. . There aetc. . There aetc. .The introduction of field data collectors into the market has improved the consistency of survey data and also resulted in a less cumbersome and systematic way of data collection. Field data collectors are devices with inbuilt GPS to collect and store details of features. These devices log the location and attribute data directly to a server or have a storage inbuilt ,7,8. MobWhen asset inventory data is collected using field survey methods, most of the time and resources are spent on field work. The office work is limited to data migration from the data collector to the servers and entry of attribute data . While uThough these field data collection techniques are widely used, the procedures are labor intensive and time consuming. There is also a considerable level of difficulty in three dimensional modelling of the assets. These limitation factors motivated technological advancements and eventually led to the development of a mobile mapping system which directly collects 3D data of the vicinity. The concept of a mobile mapping system which integrates multiple sensors on a moving platform has a history dating back to 1970s. A photo logging instrument was attached to a vehicle to collect images of transportation asset inventories. Though satellite positioning technology like GPS were unavailable during that time, a combination of accelerometers, gyroscopes and odometers were used to determine the course of the vehicle movement and direction. The photos were eventually georeferenced based on the recorded vehicle positions . CamerasMobile mapping systems reduce the data collection time significantly. Literature relating to the applications of mobile mapping system in the field of transportation are explained in detail by . With anThe survey grade mobile mapping systems are basically equipped with a commercial laser scanner, panoramic cameras, GPS-INS, dead-reckoning devices, such as odometers and a dedicated post processing software. An average absolute accuracy of 0.5 m is expected from most commercial mobile mapping systems . An impoThough mobile mapping systems reduce the data collection time tremendously, an equivalent amount of time and resources are generally spent on extracting required information from the collected data. Thus, most time and resources for creating an asset inventory is spent digitizing features and entering attribute details. Due to the intensive office work involved during data digitization, research involving semi-automatic and automatic methods of feature extraction from mobile mapping data gained importance. Different algorithms and methodologies utilizing multi-sensor data (LiDAR point clouds and images) have been proposed for road infrastructure modelling. There are also several proposed methodologies for automatic/semi-automatic detection of some urban scene features from mobile mapping data. The most prominent features include road centerlines, road boundaries, lanes, median, sidewalk, curbs, poles, signboards and roadside vegetation/trees. Depending on the type of the scene and the sensors used for data collection, there are some algorithms and concepts that are commonly used to automate feature extraction. RANSAC—RANdom SAmpling Consensus method of plane detection is used to detect and classify road planes from LiDAR data . A methoAn ideal solution for asset inventory data collection would be a fast and cost effective technology providing an easy method of data collection. Though most existing mobile mapping systems cater to the current requirements, there is a high cost involved. Time consuming manual feature extraction also adds to the disadvantage. This paper describes the process of building a cost-effective mobile mapping system using mobile LiDAR and cameras for data collection. The paper also discusses various semi-automatic and automatic techniques of feature extraction for detecting transportation assets.Transportation assets mainly consists of roads and pavements. However, other assets such as sign boards, signal lights, poles and electric, telecommunication and water utilities which are found above and below the roads and pavements also belong to the major components of transportation assets. These assets help to improve the efficiency of the roadway network thereby adding safety of transportation . AdditioOne or more Cameras—Nikon 3200, 3300: Cameras capture pictures/video frames of the scene, thereby providing the asset managers with digital pictures portraying the conditions of assets.One or more laser scanners—Velodyne HDL-32E: LiDAR records 3D point data of the vicinity in the mapping frame, which, helps create 3D model of the scene and extract features.One or more GPS receivers, Gyros, Accelerometers—Geodetics, Geo-iNav: GPS-IMU integrated solution improves the frequency of recording positions from 1 Hz (GPS) to 125 Hz. The integrated navigation solution includes the position as well as the orientation of the vehicle .On-board computer—Brix: The software for controlling the functioning of the sensors are installed on the computer. It is important to Wi-Fi enable the computer, in order to connect it remotely using a laptop/tablet.External storage device—Samsung 1TB Solid State drive (SSD): The laser scanner can record up to 700,000 points per second. The navigation data is also quite voluminous. Since, the velocity of data recording is high, it is necessary to use a Solid state drive which provides high speed data logging.The primary components of the mobile mapping system being built for the purpose of transportation asset inventory data collection includes:i.e., points from the scene within a range of 70 m from the sensor are measured. These attributes make Velodyne an obvious choice for developing low-cost mapping solution. The points are stored as frames. Each frame corresponds to a 360 degree rotation of the laser scanner in the 2D plane.The Velodyne laser scanner is widely used for mapping applications. There are a few survey grade laser scanners, such as Optech Lynx and Riegl VQ, which have accurate and high precision GPS-IMU solution. While, these scanners are very expensive, there are several inexpensive laser scanners that are cheaper than Velodyne. For instance, Hokoyu and ProtoX2D are some examples of cheap laser scanners. These scanners have just one laser diode and are mostly used for obstacle detection in robotics applications. They have a small range and insufficient accuracy for mapping applications. Velodyne has 32 laser channels covering a vertical swath of +10.67 to −30.67 degrees. The LiDAR head can rotate 360 degrees in the two-dimensional plane. The sensor works on infra-red band with wavelength of 905 nm. The sensor is light weight and compact. The HDL-32E Lidar collects up to 700,000 points per second. The time of flight method is used to determine the range of these points. The sensor typically has a range of 1 m to 70 m. Cameras have been deployed in asset mapping. Charge Coupled Device (CCD) cameras were used initially which were soon replaced by SLR cameras. The large CCD arrays and pixel size of DSLR cameras when compared to the point and shoot digital ones makes it more apt for mobile mapping applications. Nikon D3200 has a fixed focal length of 35 mm and an angle of view which is 1.5 times the format equivalent of the focal length. Nikon D3300 has a variable focal length of 15 mm to 55 mm. The angle of view is also 1.5 times the format equivalent of the focal length. Both cameras capture either 30 or 50 frames per second in the video mode depending on the chosen resolution. The duration per video is 20 min and requires about 1.3 GB of memory. Technologically advanced cameras, such as GoPro, are also widely discussed about their role in mobile mapping. However, the fish-eye lens of GoPro is prone to create distortions and decrease the quality of the cartography product.Gyroscope Dynamic Range: ±150°/s.Gyroscope Bias In-run stability (1 σ): 3°/h.Accelerometer Dynamic Range: ±3 g.Accelerometer Bias In-run stability (1 σ): <0.1 mG.The Geo-iNav instrument from Geodetics provides a GPS-IMU integrated solution. It consists of a GPS which provides positions with a horizontal accuracy of 5 cm and vertical accuracy of 10 cm on L1/L2 real time kinematic mode and horizontal accuracy of 1.5 m and vertical accuracy of 2.5 m on L1 Standalone mode. The Inertial measurement unit consists of accelerometers and gyros. The IMU has the following specifications,For a mapping application, the expected absolute accuracy of feature points is <20 cm. Hence, it is necessary to deploy an IMU with less than 3°/h of gyro bias. The Geo-iNav unit weighs 567 gram and has size of 550 cubic cm which makes it portable and apt for mobile mapping applications. It provides navigation solution at a frequency of 125 Hz. The navigation and geodetic data provided by Geo-iNav includes, navigation solution  and raw GPS/IMU data which could be used for post processing.Since the mobile mapping system could be mounted on a variety of mobile platforms from trucks to boats, it is essential to create a robust system on which these sensors could be assembled. The first important aspect to consider is the stability of the platform. The IMU, GPS antenna, cameras and the laser scanner are required to be in a stable position throughout the data collection. Additional care should also be taken to ensure that the IMU does not receive undesired vibrations due to the vehicle’s internal mechanics. The second aspect of consideration is the position of the GPS antenna. It is important to mount the GPS antenna in a position such that the signal from the GPS satellites are not occluded. The orientation of the laser scanner and cameras also play a major role in the efficiency of data collection. By changing the vertical orientation of the laser scanner, it is possible to collect points from the roads as well as overhead structures. The laser scanner is mounted on top of an arm which could rotate about the horizontal axis as shown in the GPS antenna center to the center of IMUdx, dy, dz). The body frame of the sensor platform has its positive X-axis aligned in the direction of the vehicle, positive Y-axis extends out on the side (right hand) and positive Z-axis pointing down. Based on current setup of the IMU, the negative X- axis of IMU is aligned along the forward direction of the body-frame, the Z- axis of IMU is set along the vertical axis of the body-frame but point up and the Y-axes of both body frame and IMU frame are aligned. Based on the specified mounting parameters, the GPS is located in IMU’s positive X direction. Hence, the displacement between IMU origin and GPS antenna along IMU’s X-axis should be subtracted from the sensor measurements. Thus, while computing the boresight misalignment parameters, the value of dx is negative. For similar reasons, dy and dz are positive. These translation values are used by the IMU while integrating the GPS-IMU data. The measurement should be accurate to 10 cm or less in order for the post-processing software to accurately determine the offset.The origin and axes of the IMU are marked on its box. Since, the centers of IMU and GPS antenna are physically separated and identifiable, the distance between their centers can be manually measured along the X, Y and Z directions of the IMU as shown in LiDAR (origin from which laser pulse is triggered) to the center of IMUThe origin and axes of the IMU are marked on the box. On the other hand, it is difficult to manually identify the axes of the laser scanner. Hence, the translation and rotation between the two origins are determined by using a Terrestrial Laser Scanner (TLS). The TLS and the mobile mapping unit are placed on a levelled plane. Both the mobile laser scanner and the TLS scans the vicinity. The mobile scanner is fine scanned by the TLS such that the IMU markings are clearly visible on the point cloud. Points from the TLS point cloud belonging to the IMU center and IMU axes are picked. The origin and axes of the TLS point cloud are shifted to the IMU center and axes of the mobile mapping system. Since, the scale component is fixed, 3-D Helmert rigid body transformation is applied . Now, thInterior Orientation—Interior orientation is a part of camera calibration where the measurements relating to the camera, such as the perspective center and focal length are determined. It also involves finding the scaling, skew factors and lens distortion. The interior orientation is performed in lab conditions by clicking pictures of a regular grid from different angles. By transforming the image pixels to real world lengths, the metrics of imaging can be determined.Exterior Orientation—Exterior orientation parameters change for every picture. It is the position and orientation of the camera with respect to a coordinate system, while capturing each photo. Space Resection is a conventional method used to determine exterior orientation parameters. It involves the measurement of ground control points and digitizing them on overlapping images to determine the camera position  and orientation .Camera Calibration ,40,41InOnce the components are put together on the platform, the sensors must be calibrated before they are deployed. Amongst a number of systematic errors that reflect on the data, the error due to boresight calibration is very significant. Calibration is the process of compensation for the misalignment between different sensors belonging to the system ,33,34. IThe mobile mapping system consists of two cameras, a laser scanner and a GPS-IMU instrument. Once the data collection is complete, the trajectory needs to be smoothed and the data from different sensors should be synchronized. The trajectory appears rugged mostly at places where there is a GPS signal outage. We smooth the trajectory, by creating a piecewise polynomial function.The frequency of Geo-iNav is 125 Hz. The combined solutions of GPS and IMU records a position for every 1/125th of a second. However, the Velodyne laser scanner collects up to 700,000 points per second. In order to geo-reference the points, ideally, we would need a position/orientation of the vehicle for approximately every 1/20,000th of a second. Since, the frequency of the laser scanner is much higher than that of the INS, the trajectory curve is interpolated using piecewise polynomial function in the shape of a cubic Spline (B-Spline) with timestamp (t) as its parameter as shown in Equation (1). Piecewise cubical Spline is preferred over linear interpolation because, it provides better continuity and also considered as the best fitting curve for the road trajectory ,43.(1)fCoordinates of points on the trajectory, images and LiDAR point clouds are uniquely identified by their timestamp. The GPS time from Geo-iNav is considered as the standard and the clocks of all other sensors are corrected for GPS time.The Velodyne laser scanner records two timestamps: System time in microseconds and GPS time in seconds. The system time is the number of microseconds calculated from the beginning of the hour in UTC . Thus, offset between the system time of Velodyne and GPS time is corrected using the GPS timestamp recorded by the Velodyne laser scanner.1Modified date (timestamp)—Timestamp recorded when the video was stored to the SD card (Assumed as the finishing time of the video)2Duration of the video3Frames per second (fps)A video file has the following properties that can be used to timestamp the image frames --The SLR cameras record the system time when the videos are saved to the SD card. This is arbitrarily assumed as the finishing time of a video. The beginning timestamp of the video is calculated from the duration and finishing time. Using the value of fps, the timestamp for every image frame is interpolated. The timestamp of the image frames are calculated based on the camera’s system time which is prone to have an offset from the GPS time. In order to determine the offset between camera’s clock and the GPS time, a picture of the GPS log  from the visualization screen is captured before the data collection. The difference between the system time of the camera and the GPS time on the captured picture gives the offset between the two clocks. The timestamps of the video frames are corrected for this measured offset.Direct Geo-referencing is the process of determining the position and orientation of the laser scanners and cameras (sensors) of the mobile mapping system by using the navigation data from the GPS-IMU device. The LiDAR and the cameras capture points in the scene which are external of the platform. The position and orientation of these sensors determined through direct geo-referencing techniques help in determining the coordinates of the points captured by these sensors in the mapping coordinate system .GP—Coordinate of the captured point in ECEF system,ECEFp—GNSS sensor position in global ECEF system,lGR− Rotation matrix from origin of WGS system to origin of local frame,i, lati are geodetic coordinates of ECEFp,longbaR− Rotation matrix from body frame to local frame,sbR—Rotation matrix from scanner to IMU (b-frame)—boresight rotation matrixsP—Coordinate of the point in scanner frame (as recorded by Laser Scanner)sbdT—Offset between scanner frame and IMU center (b-frame)—boresight translation.The point clouds were registered by applying the interpolated rotation values  obtained from the INS, corresponding X, Y, Z position from GPS-IMU integrated navigation data and the boresight rotation and translation between IMU and the laser scanner as shown in Equation (2).In order to improve the accuracy of direct geo-referencing, GCPs (ground control points) are collected and used to rectify any errors in the geo-referenced point cloud. It is important to choose control points that are clearly identifiable on the point cloud as well as images. The points ought to be well-distributed in the area where data is collected. The survey is carried out using GPS and total stations. The coordinates are recorded in WGS System. The measured points are identified on the registered point clouds. The point clouds are corrected by applying 3D Helmert rigid body transformation using the GCPs as parameters.etc. In order to use the direct method of geo-referencing, it is important to accurately determine the boresight misalignment parameters. That is, the separation of the sensor origin (camera) from the origin of the IMU. Since, it is unlikely to measure this separation with centimeter level of accuracy, a combination of direct and indirect geo-referencing techniques could be used in this case. Once bundle block adjustment is performed on the set of images using the GPS-IMU position data, the GCPs can be used to correct for the errors in calibration [The mobile mapping system captures images/video frames continuously. A scene is captured by multiple images with significant overlap between the frames. Hence, the first step in geo-referencing is to connect the consecutive images using common points—tie points . The traibration ,47.It is important to assure the mapping quality of the collected data before populating the asset inventory database or using them for feature extraction. The measures of quality for the survey data involves determining the relative and absolute accuracies. A particular feature might be recorded in multiple image or point cloud frames. This may be due to overlapping frames or multi-path (repeat trajectory) data. Relative accuracy is the measure of drift between the same features measured on different frames. The major cause for inconsistency between consecutive frames are systematic or random errors of the sensors involved. Positioning error will reflect in the relative accuracy of multi-path data. Features like sign boards, pavement boundaries and building corners can be used to quantify relative errors. Absolute accuracy is the closeness of the collected data to the true position of the features. In order to check absolute accuracy of the collected data, a combination of the set of GCPs used for geo-referencing and a new set of GCPs are used. The coordinates of points from the geo-referenced point cloud and images are checked against the GCPs collected thru field survey. The root mean square deviation of these two sets of values determines the absolute accuracy of the laser scanning data and images. Once, the quality of data is assured, the data is processed so that, outliers are removed and only the relevant data is used for the experiment.Laser scanning data may contain noise and erroneous returns. The first step is to filter these erroneous points. Different errors due to laser scanner and the ways they affect the quality of data are explained by . Sparse Secondly, it is important to narrow down the entire dataset to the regions related to the domain of interest and eliminate unrelated parts of the data. A transportation asset inventory database would typically contain roads and roadside features. In order to localize the point cloud, road centerline and road width are extracted from the TIGER  dataset . The TIGThe raw LiDAR data consists of returns from all features in the vicinity which includes both ground and non-ground points. In a road scene, there could be ground points, such as the road surface, sidewalk, median and there are also points from non-ground features like trees, electric lines, cars, poles, sign boards and other roadside structures. The easiest and straight-forward method to classify ground points from non-ground points is by exploiting their planarity property. Almost all ground points lie on well-defined planes. For example, the roads and pavements are typically planar. RANSAC method determines the best fit plane on a set of points. RANSAC algorithm, when run repeatedly on the outliers of each iteration, results in a number of significant planes that fits the point cloud. The points belonging to these planes are classified as the ground points (bare-earth) and the outliers of the final iteration of RANSAC are non-ground points. Thus, ground points can be differentiated from non-ground points using this procedure.The concept of using GIS technology for Asset Management is well-established. GIS provides a concrete mapping between spatial data and its corresponding attribute details, which helps in making well-informed decisions about infrastructure management and maintenance . DisplayAbility to manage voluminous data efficiently—large point cloudsetc.Maintain consistency between multi-sensor data—images, LiDAR point clouds, thermal/hyperspectral data Appropriate versioning of collected datasets.Implementation of basic automatic feature extraction algorithms and interface to manually digitize features.Relational database design to store assets and their spatial relationships.Visualization, rendering and fluid user interface.As discussed in the previous sections, most transportation asset management frameworks use the traditional methods of data collection using GPS and other field data collectors. The data is then imported into CAD format which is later converted into GIS compatible formats. The pioneers in asset management software support almost all data formats belonging to common field data collection methods. However, the best practice of integrating data from mobile mapping systems directly into a GIS database is a state-of-the-art research domain. The structure of databases and spatial relationships between the assets do not depend on the data collection methodology. However, mobile mapping system is not a direct method of data collection. It involves a number of processing tasks before documenting the actual assets of interest. It involves tasks that range from geo-referencing to feature extraction. An ideal framework for managing asset data collected using a mobile mapping system should include these processing tasks along with the implementation of a GIS database for asset inventory. Characteristics of an asset management framework using mobile mapping data:The mobile mapping system with the described components are assembled as shown in Once, the data collection is complete, the trajectory is post-processed to apply differential corrections and to integrate GPS-IMU using “GeoRTD” from Geodetics. The data is converted into appropriate format for synchronizing with data from other sensors. The GPS-IMU data which is originally in binary format is converted into ASCII (American Standard Code for Information Interchange). The point cloud data from laser scanner is converted from PCAP (Packet Capture) to CSV  format. Further, the videos recorded by the camera in MP4 (MPEG-4 AVC -- Advanced Video Coding) format are converted to individual frames which are images in JPEG (Joint Photographic Experts Group). The data from different sensors are synchronized based on the recorded timestamp. The system time of all sensors are measured in UTC system. The data is compensated for the 17 s offset measured between UTC and GPS time. However, there can also be a possible drift between the internal system time recorded by the cameras and GPS time. A drift of 24.5 s and 28.5 s are measured between the system times of the two cameras and the GPS time respectively. At the end of this synchronization step, each return from the laser scanner, each frame recorded by the camera and each position recorded by the GPS-IMU sensors will be timestamped based on GPS time. A piece-wise polynomial, spline function is fitted for X, Y, Z, heading, roll and pitch values with timestamp as the parameter. Thus, the navigation data corresponding to each point from the laser point clouds/each image can be determined from the spline functions based on their respective timestamps. Additionally, the rotation and translation values corresponding to boresight misalignment/sensor calibration are also applied. Once the data is geo-referenced, the point clouds that are initially in sensor’s coordinate system were converted to a global coordinate system.Ground Control Points are required to determine the exterior orientation (EO) parameters of images and also to check the quality of geo-referenced point clouds. Points that are well-defined on the ground, on images and on point clouds are considered for GCPs. GPS and Total Stations are used to measure GCPs. Based on rough reconnaissance of the area, GPS stations are determined. There are locations from which maximum number of GCPs lie in the vicinity. The GPS instrument is set up on these locations in static mode to record the station coordinates. A total station is then set up at these locations to determine the coordinates of the GCPs. The EO parameters of the images are determined by using the ground control points in addition to calibration values and navigation data. The quality of geo-referenced point clouds is determined by calculating the root mean square deviation between the measured GCPs and the corresponding points on the point cloud.The data collection was carried out by mounting the system on a truck a and a gThe laser scanning data is stored as number of frames with each frame having a time range of 0.2 s. The density of the point cloud depends on the number of returns. Since, the scanner is oriented at an approximate angle of 135 degrees as shown in The LiDAR frames are loaded in Leica Cyclone environment to digitize the features of interest. The assets of interest for transportation inventory management includes, sidewalks, medians, guardrails, fencing, misc. concrete structures, lighting, landscape areas, delineators, striping, symbols and messages, crosswalks, stop bars, raised pavement markers, attenuators and highway signs. In addition to the spatial component, the attributes for these assets are also populated. The typical database structure for transportation asset inventory data management includes attributes, e.g., condition and dimensions. However, road signs should also be tagged with the symbol represented by the sign. The spatial component of the assets may be points, lines or polygons depending on its geometry and may also require some customized attributes. Hence, each asset is stored in an individual table with attributes as shown in As discussed in the previous section, mobile mapping reduces the data collection time greatly. However, digitizing individual features from the geo-referenced point clouds and images requires huge amount of human effort and is time-consuming. Since, we have cameras and a laser scanner onboard, it improves the scope for automatic/semi-automatic feature extraction. Algorithms involving geometry of feature points can be used in combination with image processing algorithms to get better results. There are a number of different methods being proposed by researchers in geomatics and data mining scientists for automatic feature detection and extraction. Some common methods used in feature detection that can be used to extract different transportation asset inventories are described in this section.The major advantage of a mobile mapping system with multiple sensors onboard is that the data from each sensor complements the other and helps extract maximum information about the scene. Implementing a combination of image processing and geometry algorithms on images and point cloud data helps in detecting the road markings with almost nil manual intervention. The edges of the sidewalk detected from the images are used to define the boundary of the road and the bare-earth LiDAR points within the boundary are considered as road points. In order to ensure clear visibility for the drivers, road lane markings have a much higher reflectance when compared to the road surface. The boundaries of the road markings can be extracted from the images  as well Road signs are important components of the transportation asset inventory database. Road signs are non-ground points and do not lie on a definite plane. They are made up of aluminum which is highly reflective and also clearly distinguishable on the point cloud. The shape of the sign board can be determined from the boundary of clusters of high intensity returns ,51. SignSeveral methods utilizing the geometry of point clouds and also the intensity of returns are used to extract assets like poles and lamp posts. Considering a high density LiDAR point cloud, the poles and lamp posts can be modeled as cylinders with a height and volume threshold . HoweverField data collection methods possess a number of potential challenges such as high data collection time, increased manual effort, safety constraints and limitation of information collected. Additionally, several data management aspects like data transformation, storage and integrity are not efficiently streamlined when field data collection methods are used. For instance, the workflow currently adopted by the FDOT (Florida Department of Transportation) involves field data collection through traditional survey methods. The data is converted into CAD (Computer-Aided Design) format; the images captured are converted to appropriate GIS raster format and are used as an overlay on as-built drawings. These methods often involve multi-step processes which are difficult to automate for an organized work-flow. It is also challenging to integrate newly collected data with existing data. A mobile mapping technology overcomes almost all of these limitations. Though the initial cost of the system is high, once the system is built, it makes frequent data collection easier and helps procure up-to-date information. The experiment developed a very economic system whose cost sums up to 65,000 USD. Considering the recurring cost of man power involved in traditional field survey methods, the initial cost involved with a mobile mapping system providing mapping grade accuracy turns out to be optimally advantageous. Major challenges involved in using a mobile mapping system is the processing of voluminous data and laborious digitization of individual features. These limitations can be overcome using improved data management techniques and automatic feature extraction methodologies. The use of field data collection methods using improved positioning instruments and hand-held mapping devices is a huge leap from traditional surveying methods in the last decade and now, mobile mapping system is the newer mapping technique owing to the number of advantages over its predecessors.Less laborious data collection process.Possibility of frequent data collection for constantly changing scenariosData can be reviewed for condition assessment at the comfort of officeMinimum field work involvedDirect and accurate elevation information from mobile LiDAR dataEasy to conduct survey over inaccessible areas.Minimum/nil hindrance to commuters and traffic.Advantages of a mobile mapping system:Considering the importance of a detailed asset inventory database and the limitations of traditional surveying methods to achieve that, there is a need for an economic solution with improved capabilities. Eventually, a cost-effective mobile mapping system was built to provide a solution to the limitations posed by the previous techniques. The geo-referenced data from the mobile mapping system provides a three-dimensional model of the entire scene. This provides a direct method to accurately determine the three-dimensional coordinates of road assets in the corridor. Though, there is no single well-established methodology for automatic extraction of features from the point clouds and images, a number of proven techniques to detect individual assets have been demonstrated with the experimental data. Additionally, the advantages of mobile mapping system over other techniques makes it an important technology for asset inventory mapping. The current framework includes geo-referencing tools and quality check methods for point clouds and images. The current research involve building a UAV-based mobile mapping system to monitor the condition of overhead road infrastructure. The future work will also include developing more automated feature extraction methods to further improve office processing cost in asset inventory management.


Health-care waste (HCW) management is a major challenge for municipalities, particularly in the cities of developing nations. Selecting the best treatment technology for HCW can be regarded as a complex multi-criteria decision making (MCDM) issue involving a number of alternatives and multiple evaluation criteria. In addition, decision makers tend to express their personal assessments via multi-granularity linguistic term sets because of different backgrounds and knowledge, some of which may be imprecise, uncertain and incomplete. Therefore, the main objective of this study is to propose a new hybrid decision making approach combining interval 2-tuple induced distance operators with the technique for order preference by similarity to an ideal solution (TOPSIS) for tackling HCW treatment technology selection problems with linguistic information. The proposed interval 2-tuple induced TOPSIS (ITI-TOPSIS) can not only model the uncertainty and diversity of the assessment information given by decision makers, but also reflect the complex attitudinal characters of decision makers and provide much more complete information for the selection of the optimum disposal alternative. Finally, an empirical example in Shanghai, China is provided to illustrate the proposed decision making method, and results show that the ITI-TOPSIS proposed in this paper can solve the problem of HCW treatment technology selection effectively. With the rising awareness of the environmental implications of waste disposal, the management and treatment of health-care wastes (HCWs) are gaining more attention all around the world ,2,3,4. Het al. A2≻A3≻A1≻The problem of medical waste requiring specialized treatment and management of its disposal is growing rapidly as a direct result of fast urbanization and population growth, especially in the developing world. In this paper, we proposed a hybrid decision making approach that uses interval 2-tuple induced distance operators and the TOPSIS method to solve the HCW treatment technology selection problem with linguistic information. Particularly, we introduced a new type of distance operator called the ITIOWD operator to calculate the separate measures of each alternative from the positive-ideal and the negative-ideal solutions. The ITI-TOPSIS method is able to model the uncertainty and diversity of assessment information offered by decision makers and can consider the complex attitudinal character of the decision maker in the decision process. Besides, it can provide much more complete information for decision making because it is able to consider a lot of different scenarios according to the interest of the decision maker. Finally, the feasibility and applicability of the proposed method have been illustrated with an empirical case study in Shanghai, China. The results showed that the proposed method for group decision making with interval 2-tuple linguistic information can effectively deal with the HCW disposal technology selection problem under uncertain and complex environments. This study has several implications for possible directions of further research. First, the weights of the selection criteria are given directly in this study. But, in many practical situations, it is normally difficult to determine the criteria weights subjectively. Therefore, in the future, it is suggested to develop an optimization model so as to derive the weighting vector of selection criteria objectively. Second, the criteria are assumed to be independent in the proposed method when modeling the HCW treatment technology selection problem. In actual cases, however, various types of relationships may exist among evaluation criteria. Accordingly, a modified model to deal with the interdependence among different criteria should be investigated in future research. In addition, we expect to consider the potential applications of the developed ITI-TOPSIS method to other decision making problems, such as material selection, factory location and personnel evaluation, to further validate its applicability and effectiveness.


Minmi is the only known genus of ankylosaurian dinosaur from Australia. Seven specimens are known, all from the Lower Cretaceous of Queensland. Only two of these have been described in any detail: the holotype specimen Minmi paravertebra from the Bungil Formation near Roma, and a near complete skeleton from the Allaru Mudstone on Marathon Station near Richmond, preliminarily referred to a possible new species of Minmi. The Marathon specimen represents one of the world’s most complete ankylosaurian skeletons and the best-preserved dinosaurian fossil from eastern Gondwana. Moreover, among ankylosaurians, its skull is one of only a few in which the majority of sutures have not been obliterated by dermal ossifications or surface remodelling. Recent preparation of the Marathon specimen has revealed new details of the palate and narial regions, permitting a comprehensive description and thus providing new insights cranial osteology of a basal ankylosaurian. The skull has also undergone computed tomography, digital segmentation and 3D computer visualisation enabling the reconstruction of its nasal cavity and endocranium. The airways of the Marathon specimen are more complicated than non-ankylosaurian dinosaurs but less so than derived ankylosaurians. The cranial (brain) endocast is superficially similar to those of other ankylosaurians but is strongly divergent in many important respects. The inner ear is extremely large and unlike that of any dinosaur yet known. Based on a high number of diagnostic differences between the skull of the Marathon specimen and other ankylosaurians, we consider it prudent to assign this specimen to a new genus and species of ankylosaurian. Kunbarrasaurus ieversi gen. et sp. nov. represents the second genus of ankylosaurian from Australia and is characterised by an unusual melange of both primitive and derived characters, shedding new light on the evolution of the ankylosaurian skull. Minmi—the only genus of ankylosaurian known from Australia. This assignment was based solely on its possession of paravertebral elements , which were a diagnostic feature of Minmi paravertebra  was found in the upper Albian–(?)lower Cenomanian Allaru Mudstone on Marathon Station near Richmond, north-western Queensland, Australia by Mr. Ian Ievers . The fosvertebra . A complMinmi, in most instances tentatively. Only two of these specimens have been described in any detail: the holotype Minmi paravertebra QM F10329 from the lower Aptian  Walloon Coal Measures at Balgowan, Darling Downs, Queensland  Long, 1.The Marathon specimen (QM F18101) represents the most complete dinosaurian fossil from eastern Gondwana , and the most complete ankylosaurian fossil from Gondwana. It is also one of the world’s most complete ankylosaurians. Moreover, it includes one of the few ankylosaurian skulls in which most of the sutures have not fused or been obliterated by dermal ossifications or remodeling of bone .Full preparation of the skull of QM F18101 has now been completed. In light of this advance, the aim of this paper is to provide a comprehensive description of the cranial osteology of QM F18101, in particular that pertaining to the nasal and palatal region. To this end, Computer Tomographic (CT) scanning, as well as digital segmentation and 3D computer visualisation of the endosseous cavities has also been undertaken. The results additionally enable us to compare QM F18101 with other ankylosaurian crania that have undergone similar analysis. The new description also allows for the taxonomic reassessment of QM F18101.Scutellosaurus lawleri, Emausaurus ernsti and Scelidosaurus harrisonii) collectively form the clade Thyreophora  . Ankylosreophora . Ankylosreophora . Howeverreophora .Minmi (excluding those conducted by Minmi paravertebra (QM F10329) and the Marathon specimen (QM F18101) as the same genus, despite the fact that the latter was only tentatively assigned to Minmi based on one defining feature (discussed above) and was not fully described or taxonomically assessed. Despite this, the specific phylogenetic position of Minmi has varied between analyses. This is most likely due to the limited published information on QM F18101’s osteology, both for the skull and the postcranium, as well as the fragmentary condition of Minmi paravertebra. Generally, Minmi has been assigned to two relatively basal positions within Ankylosauria. The taxon has been regarded as a stem ankylosaurian—that is positioned basal to the two major ankylosaurian clades . The online version of this work is archived and available from the following digital repositories: PeerJ, PubMed Central and CLOCKSS.The electronic version of this article in Portable Document Format (PDF) will represent a published work according to the International Commission on Zoological Nomenclature (ICZN), and hence the new names contained in the electronic version are effectively published under that Code from the electronic edition alone. This published work and the nomenclatural acts it contains have been registered in ZooBank, the online registration system for the ICZN. The ZooBank LSIDs (Life Science Identifiers) can be resolved and the associated information viewed through any standard web browser by appending the LSID to the prefix Pinacosaurus  are approximately the same size  despite the fact that they originate from different locales and time periods, with the implication being that it would be highly unlikely that only juveniles were preserved. Furthermore the majority of early forms of ankylosaurs are small , such that histological investigations were not possible. A more detailed study of the ontogenetic status of QM F18101 is currently being undertaken with the description of its postcranial elements. For the present study, we concur with A bone microstructure analysis was planned for this study in an effort to further assess the maturity of the specimen. Unfortunately preliminary testing of both Kunbarrasaurus ieversi gen. et sp. nov.Minmi sp.  word for ‘shield’, and souros  (σαυρoς), the Greek word for ‘lizard’, and is a reference to the animal’s heavily ossified skin. The species name honours Mr Ian Ievers, discoverer of the holotype. The name therefore means ‘Ievers’ shield-lizard’.Holotype. QM F18101, a near-complete ankylosaurian dinosaur specimen that includes most of the skull and mandible, along with an articulated postcranium.Diagnosis . The cranial roof is flat ; the dorsal surfaces of the prefrontal, supraorbital and postorbital elements form an approximately 90° angle with the lateral surfaces of the skull; the supraorbital comprises a single element; the prefrontal is restricted to the dorsal surface of the skull and does not contribute to the orbit; the nasals do not contribute to the lateral surface of the skull, being restricted to the dorsal surface and the medially displaced nasal vestibule; the nasal vestibule  is large in proportion to the maxillary rostrum, fully exposed dorsally and laterally; the maxilla extends for the entire dorsoventral height of the skull and contacts the cranial roof at the prefrontal; the caudal end of the tooth row terminates ventral to the caudal margin of the orbit; the lacrimal is vertically  orientated; the pterygoids do not contact one another caudally near the braincase and are completely separated by the basisphenoid; the long axis of the quadrate is vertically aligned; the coronoid process is very pronounced; the sidewall of the braincase is poorly ossified, such that many of the cranial nerves must have traversed large bony apertures rather than the discrete foramina; the inner ear is extremely large in proportion to the skull and morphologically unlike any other known dinosaur in that the vestibular region is not separated by bone from the endocranial cavity, the ventral cochlear region is poorly ossified, and the vestibular region is so expanded that the semicircular canals appear very short; cranial dermal ossifications are either flat or with a very shallow keel; no quadratojugal and squamosal horn or boss-like dermal ossifications are present.Locality and horizon. Marathon Station (south of the Flinders River) east of Richmond, north-western Queensland, Australia; Allaru Mudstone, uppermost part of the Wilgunya Subgroup of the Rolling Downs Group of the Eromanga Basin.The Allaru Mudstone conformably overlies either the Toolebuc or the Wallumbilla formations, and is conformably overlain by the Mackunda Formation  Fig. 1)Fig. 1). Platypterygius longmani?Notochelone costataMarine phytoplankton dominance fluctuates throughout the Allaru Mudstone, reflecting varying depositional environments. Numerous well-preserved marine macrofossils occur within it, including bivalves, gastropods and belemnites . TeleostMuttaburrasaurus sp. lower Cenomanian (Phimopollenites pannosus spore-pollen zone (upper Albian: Hoegisporis uniforma (formerly the Appendicisporites distocarinatus) spore-pollen zone .The skull of QM F18101 is approximately 90 per cent complete  and 3. TThe apex of the maxillary rostrum is not preserved, such that the nares as well as the rostral-most parts of the premaxillae, maxillae and nasals are missing. The left premaxillary ‘beak’ was removed from the nasal vestibule during preparation of the skull see . The rigThe left lateral side of the skull is well preserved  and 3B, The right lateral side of the skull has been crushed ventromedially  and 3D, in vivo position. Extensive fracturing obscures the sutural boundaries of the left ectopterygoid. The roots of fourteen teeth remain in their alveoli in the right maxilla, and twenty in the left maxilla. Some disarticulated and broken crowns and roots are scattered on the maxillae. The maxillary secondary palate and the vomer exhibit fragmentation as a consequence of the medially turned right maxillae. The caudal edge of the maxillary secondary palate and the vomer are not preserved (i’), whereas the squamosal-articular end of the right quadrate underlies the right pterygoid but overlies the lateral end of the right otoccipital (ii’).In ventral aspect  and 2D, reserved  and 2D. reserved  and 2D. reserved  and 2D. reserved  and 2D. reserved  ‘qi’, whccipital  ‘qii’.Although the lateral walls of the braincase  and 2D hIn caudal aspect, all elements are preserved with some minor fragmentation see  and 3H, The dentary and the coronoid process of the left mandibular ramus are partially preserved, whereas only a portion of the dentary from the right mandibular ramus remains.Overall, the skull is approximately 30 per cent longer than it is wide. In dorsal aspect, the skull has a pentagonal outline, with the greatest width occurring immediately caudal to the orbits. In caudal aspect the occiput is transversely wider than it is high dorsoventrally  and 3H, The premaxilla comprises a main lateral body and a rostral beak. The main body of the premaxilla forms approximately 25% of the total length of the preserved portion of the skull in lateral aspect –3D. The The preserved portion of the left premaxillary beak  has a trThe maxilla contributes approximately one-third of the total length of the preserved portion of the skull in lateral aspect –3D, and Pinacosaurus (AMNH 6523), Ankylosaurus (AMNH 5214), and Euoplocephalus (AMNH 5405), whereas it becomes narrow in Saichania (PIN N 3142/250). The tooth row shows a slight hour-glass shape, in ventral aspect. The lateral divergence of the tooth rows at their caudal ends is considerably wider than the rostral end. The caudal end of the tooth row is unusual in that it is below the caudal margin of the orbit. More typically the tooth row ends well before the orbit , below the rostral margin of the orbit , or below the orbit  of QM F18101 has at least one replacement alveolus, but this number increases caudally to as many as three.In ventral aspect, the maxilla underlies the orbit and part of the jugal, thus the orbit is obscured from view. The pterygoid forms the caudal border to the maxilla. In ventral aspect, the tooth row is inset from the lateral edge of the maxilla. As the rostral portions of the lateral maxillary shelves  are slightly damaged, their exact width cannot be determined. Caudally, the shelf remains wide as in  Tarchia ). There aichania , 19 in Mgantegia  and alsolosaurus , 17 in Pcosaurus , 16–17 ikielanae , 34–35 ilosaurus , and 19–cephalus . Thus, aBetween the tooth rows, the medial extensions of the maxillae form the maxillary secondary palate. These medial extensions are separated by a thin, vertically orientated bone, most likely the vomer. The caudal edge of the maxillary secondary palate forms the rostral and lateral borders of the primary choanae . This maxillary secondary palate extends from the rostral tip of the maxilla to a point just rostral of the orbits. The maxillary secondary palate forms a horizontally flat plate, and is the ventral surface of the nasal cavity.Gastonia (CEUMP 1307) and Gargoyleosaurus (DMNH 27726) in that the crowns of QM F18101 have a well-developed cingulum on both sides, although not as bulbous as that of some nodosaurids  or some ankylosaurids (Saichania MPC 100/151). The teeth are more nodosaurid-like than ankylosaurid-like in the reduced number of marginal denticles  and in some cases ossify .The nasal comprises over one-third of the skull in rostral aspect  and 3F aPinacosaurus  –3D.Pinacosaurus  plate that is divided medially by the basisphenoid –3H. The The rostral (vomerine) process of the pterygoid arises from the rostral edge of the main body of the pterygoid, most likely near the junction with the ectopterygoid  and 2D. The rostral (vomerine) processes of the pterygoid were originally thought to be the palatines, since in ankylosaurians the palatine and pteryoid form a broad, rostrally extensive shelf  that obscure the parasphenoid from view. However compared with other dinosaurs, particularly stegosaurians and other ornithischians, the structures seen in QM F18101 are more similar to the rostral processes of the pterygoids and will thus be interpreted as such herein . UnfortuThe area in which the ectopterygoid is most likely to exist  is highly fractured, thus the boundaries for this element are unclear, but are visible in some parts of the CT data . It may The quadrates are nearly vertically orientated, rostrocaudally flattened and are very slightly concave rostrally and convex caudally –3H. The The supraoccipital bone was adequately described in As in virtually all posthatchling archosaurians, including other ankylosaurians, the exoccipital and opisthotic elements are indistinguishably fused into a single compound element referred to as the otoccipital . The otoThe prootic is visible only in the CT scan data –5F. The The laterosphenoid is visible only in the CT scan data . As noteThe orbitosphenoid is obscured by matrix and only visible in the CT scan data . Some e have sugThe external ventral surface of the basioccipital is discussed in detail by As just noted, contrary to The parasphenoid is a fused element, which projects rostrally from the basisphenoid along the midline between the interpterygoid vacuity  and 2D. The preserved portions of the left mandibular ramus (dentary + coronoid process) are described in detail by The majority of the dermal ossifications of QM F18101 were described in The dermal ossifications of the orbit and nasal regions were not discussed by Pinacosaurus (Pinacosaurus grangeri (IVPP V16853)  : Fig. 2.Previously described by The nasal cavity of QM F18101 presents some challenges to interpret given that (1) ankylosaurians in general have unusual and highly divergent nasal cavities that loop through the snout , (2) QM Pinacosaurus Pinacosaucephalus , and indcephalus . The chocephalus .The course of the airway between the nostril and choanae is not entirely clear but some options are presented here . As showFrom this chamber, the airway may simply have passed ventromedially to reach the choana. In this non-looping-airway hypothesis  and 9E, Euoplocephalus , the olfactory tract is relatively short. The cerebral hemispheres are clearly indicated on the endocast as lateral swellings in the rostral portion of the endocast, but distinct optic lobes and cerebellum are not grossly observable, which is typical of other ankylosaurians . QM F18101 displays the primitive condition in this regard, resembling Stegosaurus and most other dinosaurs , the orbits and eyeballs are more caudally positioned within the skull, resulting in the lateral course of not just the optic nerves, but also the nerves supplying the extraocular muscles . The canals for these nerves in QM F18101 are not as discrete as in other ankylosaurians but what is preserved is consistent with QM F18101 retaining a more plesiomorphic condition, suggesting that, as with the optic nerves, the extraocular muscle nerves were not directed strongly laterally. As noted above, the side wall of the braincase is not as ossified as in other ankylosaurians  in archosaurians emerges through a foramen between the laterosphenoid and prootic  but in Qve three . Finallycephalus  and, as Euoplocephalus  or Panoplosaurus , the inner ear is much larger in absolute size  sufficiently distinct from other ankylosaurian taxa to warrant referral to a new genus and species: Kunbarrasaurus ieversi gen. et sp. nov. Furthermore preliminary investigations into the postcranial elements of QM F18101 reveal that a number of significant differences exist between it and the holotype of Minmi paravertebra (QM F10329), but this is part of an ongoing study that will be presented elsewhere. Accompanying the description of the postcranium of K. ieversi will be an in-depth analysis into its phylogenetic position.Based on the clear number of osteological differences, and thus diagnostic characters, between the cranium of QM F18101 and other ankylosaurians (see ‘Diagnosis’ and discussed above) we consider the specimen known as Minmi paravertebra (QM F10329) has recently being assigned to nomen dubium by Minmi paravertebra comprises postcranial material only, an in-depth comparison and reappraisal of the specimen will be conducted in conjunction with the redescription of the postcranium of QM F1810; something that is beyond the scope of this study. Pending the forthcoming publication, we still consider Minmi paravertebra sufficiently different to other ankylosaurians, particularly within Australia and other Gondwanan landmasses, to represent a distinct genus. Consequently Kunbarrasaurus represents the second named genus of ankylosaurian from Australia.Of note, K. ieversi exhibits characteristics of both non-eurypodan thyreophorans and ankylosaurians (discussed above). Of those non-eurypodan thyreophoran and ankylosaurian taxa for which cranial sutures are visible, the overall arrangement of the cranial bones and general shape of the skull of QM F18101 is more similar to Scelidosaurus harrisonii  and Cedarpelta bilbeyhallorum (Pinacosaurus (K. ieversi) is typically recovered as a basal ankylosaurian .The pattern of fusion of the skull of hallorum , with mohallorum . HoweverK. ieversi appears to be associated with an expansion of cranial bones, as there is no evidence of bone thickening and the skull possesses few dermal ossifications and none within the vicinity of the antorbital fenestrae. More specifically, the closure of the antorbital fenestrae in K. ieversi would have resulted from the caudal expansion of the maxilla and the ventral extension of the lacrimal, since the jugal is not as rostrally extensive as that of other thyreophorans . Unlike some ankylosaurians  and periosteal osteogenesis  “dermat. K. ievefication .K. ieversi in most phylogenetic analyses has made it difficult to interpret some of the potentially more divergent aspects of its morphology. For example, with regard to the nasal cavity, given that K. ieversi may represent an outgroup to ankylosaurian taxa known to have complicated looping nasal passages, it is phylogenetically ambiguous whether a looping airway should be expected. Unfortunately, the preserved morphology is no more definitive. A valid hypothesis is that K. ieversi lacked the nasal convolutions of more derived ankylosaurians and instead had a simpler airway that passed from nostril to choana, with a paranasal air sinuses that probably would be the homolog of the typically archosaurian antorbital sinus; this arrangement was in fact expected prior to the discoveries provided by CT scanning and 3D visualization  morphology, some aspects of the braincase are so unusual that they defy comparison. For example, although the laterosphenoid—a synapomorphy of archosaurians  —is presinosaurs  in that K. ieversi to QM F18101 (formerly known as Minmi sp.) is based on a significant number of features that distinguish it from other ankylosaurians. Many of the cranial sutures of K. ieversi have not fused, nor are they obscured by dermal ossifications. The closure of the antorbital and supratemporal fenestrae of K. ieversi is most likely due to the expansion of cranial bones, and not the result of overgrowth of dermal ossifications. The ornamentation of the skull of K. ieversi is the result of both epidermal ossification (osteoderms) and periosteal osteogenesis. Some aspects of the nasal cavity remain obscure, but there is enough evidence to suggest that K. ieversi had a more complicated airway than in non-ankylosaurian outgroups. It is presently unclear whether K. ieversi had a convoluted, looping nasal passage to the extent seen in more advanced ankylosaurians. Some aspects of the braincase are potentially unique among known species, such as the unusual inner ear, which is not only extremely large but also has a divergent morphology due to lack of ossification medially and ventrally.The assignment of a new genus and species name 10.7717/peerj.1475/supp-1Appendix S1Click here for additional data file.10.7717/peerj.1475/supp-2Supplemental Information 1Click here for additional data file.


Ground bearing capacity has become a relevant concept for site-specific management that aims to protect soil from the compaction and the rutting produced by the indiscriminate use of agricultural and forestry machines. Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming. In order to alleviate these difficulties, this paper introduces an innovative sensory system based on Visible-Near InfraRed (VIS-NIR), Short-Wave InfraRed (SWIR) and Long-Wave InfraRed (LWIR) imagery and a sequential algorithm that combines a registration procedure, a multi-class SVM classifier, a K-means clustering and a linear regression for estimating the ground bearing capacity. To evaluate the feasibility and capabilities of the presented approach, several experimental tests were carried out in a sandy-loam terrain. The proposed solution offers notable benefits such as its non-invasiveness to the soil, its spatial coverage without the need for exhaustive manual measurements and its real time operation. Therefore, it can be very useful in decision making processes that tend to reduce ground damage during agricultural and forestry operations. The utilisation of wheeled and tracked vehicles is a widespread practice in agriculture and forestry applications . HoweverGround bearing capacity can be defined as the ability of the soil to carry a certain weight without being damaged . In foreOn the other hand, the capability of the soil to bear an specific weight without suffering from compaction and/or rutting is predominantly determined by the soil type and the water content ,18. SincSeveral techniques have been proposed for measuring the soil water content, including direct, indirect and remote sensing methods ,20,21. DThe thermogravimetric technique is the oldest established and the only truly direct method for determining the total moisture content of soils. The method implies to oven-dry a soil sample of known weight at a constant temperature of 105 °C. After that, the sample is cooled and reweighted. The water content of the sample is then given by the mass of water per unit mass of dry soil ,19,21.241Am) is inserted into the soil, from which fast neutrons are emitted. When these particles collide with the hydrogen atoms present in the water molecules, they lose energy and slow down. Therefore, the total amount of water in the soil is measured taken into account the rate of neutron slowdown [The most common indirect methods for estimating the soil water content are the neutron scattering, the use of porous blocks, the dielectric measurements by means of capacity sensors and Time-Domain Reflectometry (TDR) ,23. The slowdown ,21,24. Pslowdown . Based oslowdown ,27,28. Tslowdown ,30.Although non-invasive techniques have attracted a lot of attention in the last decade, they are not well established yet. Among these techniques the NIR spectroscopy, the use of Ground Penetrating Radars (GPRs) and remote sensing are noteworthy. In  a fiber-This paper proposes an arrangement composed of a SWIR camera, a LWIR camera and a VIS-NIR system that allows interchanging two band-pass optical filters with centre wavelengths of 624 nm and 950 nm and an associated algorithm for the estimation of the ground bearing capacity. The presented approach represents a non-contact method that enables to acquire measurements before traversing the field. In this way, no disturbance of the site will be produced and ground damages can be significantly reduced. The rest of the paper is organised as follows: The proposed solution is based on the combination of a P25 LWIR camera  a GoldEye P-032 SWIR camera  and a VIS-NIR system that comprises an Allied Vision AVT Prosilica GC2450 high resolution monochrome camera, a custom-made filter wheel, and a servomotor that allows interchanging two band-pass optical filters with centre wavelengths of 624 nm and 950 nm see . The FLIBand-pass optical filters with centre wavelengths of 624 nm and 950 nm are selected to enable discrimination between vegetation and ground. It is well documented that all photosynthetic plants are characterised by a low reflectance in red wavelengths (600 nm–700 nm) because chlorophylls (and related pigments) absorb much of the incident energy for photosynthesis. Meanwhile, in the near-infrared wavelengths (700 nm–1300 nm) photosynthesising plants reflect large proportions of the incident sunlight ,41. Ther2O; symmetric and asymmetric O–H stretching and O–H bending [Once these areas of interest have been isolated, they are analysed on SWIR and LWIR images in order to study the surface soil moisture. Water strongly absorbs light in the SWIR wavelengths. This is due to overtones and fundamentals of the three vibration frequencies of H bending ,44. Thus bending  due main2. For each test the cone was pushed into the soil at constant velocity and the penetration resistance was read at certain depths.It is also important to mention that during the design phase several measurements were carried out not only with the proposed sensory rig, but also with a penetrometer, so that the soil penetration resistance acquired with the penetrometer could be associated with the normalised reflectance value of the SWIR images. The M06.01 penetrometer  utilised during these tests for the acquisition of training data is shown in A registration procedure. VIS-NIR, SWIR and LWIR images come from cameras that exhibit different field of view and different pixel array. To overcome this problem, the random sample consensus (RANSAC) algorithm  is adoptA multi-class Support Vector Machine (SVM) classifier that is applied to registered monochrome, 624 nm and 950 nm images and trained to label the pixels of these images into three classes that are: soil, vegetation and others. Two SVMs are executed sequentially, each one for detecting a class against the rest, as described in . The resThe masking of the registered SWIR and LWIR images by using the pixel-based classification map obtained in the previous step.A K-means clustering that is applied to the soil pixels of the masked thermal image with the aim of partitioning soil pixels into three effective clusters. These three clusters are then utilised for delimiting the areas where the mean temperatures and the mean reflectance values will be calculated from the thermal and the normalised SWIR images, respectively. Next, the absolute differences from the mean values of the clustered areas are calculated and compared with certain predefined thresholds that will help us to determine if the soil sample has a homogeneous bearing capacity, or if on the contrary, the soil is heterogeneous. If soil is homogeneous a unique mean reflectance value is calculated from the soil pixels of the normalised SWIR image. If the soil is heterogeneous, a mean reflectance value is calculated for each area of the normalised SWIR image. Delimitation of these areas is given by the clusters obtained from the masked thermal image after applying the K-means clustering.A linear regression that is applied for modelling the relationship between the soil penetration resistance and the mean reflectance value of the areas of interest from the corresponding normalised SWIR image. For accomplish this linear regression, numerous experimental tests were carried out, where the penetration resistance was measured with the penetrometer and the corresponding mean reflectance value was calculated from the normalised SWIR image acquired for the same scenario. Therefore, mean reflectance values obtained in the previous step are used for the final estimation of the ground bearing capacity.Thus, the algorithm proposed for estimating the ground bearing capacity combines: The methodology proposed for estimating the ground bearing capacity is summarised in Data acquisition was conducted in several campaigns that took place between March and July of 2014, in Madrid, Spain. Sites include CSIC premises and two field stations owned by CSIC at Arganda del Rey , totalling an extension of 64 ha on a typical xerofluvent soil with a sandy-loam texture in the first 0.5 m and increasingly sandy texture below. For a better comprehension, the description of the experimental stage is divided into four main phases that are detailed below.The first phase of the experimental stage was devoted to the acquisition of images for implementing the registration procedure. Although the VIS-NIR, LWIR and SWIR cameras that constitute the proposed multisensory system are utilised for acquiring images from the same scene, resulting images are taken from slightly different viewpoints, with a different field of view and different resolution. Thus, this procedure aims to obtain a direct correspondence between the pixels of the different images before continuing with further processing steps for estimating the ground bearing capacity. The dataset acquired in this first phase included monochrome, LWIR and SWIR images. A chessboard pattern was utilised for facilitating the process of finding the point correspondences. Since in natural scenarios soil is merged with vegetation and other elements, the purpose of the second phase of the experimentation stage was to train and evaluate the multi-class SVM classifier for discriminating the soil from the rest of the scene elements. In this way, only pixels belonging to soil are analysed in LWIR and SWIR imaging during the other processing steps of the proposed algorithm.For training the SVMs, three acquired dataset were randomly picked. Each dataset acquired in this phase included a monochrome image and two images with band-pass filters that have centre wavelengths of 624 nm and 950 nm. From these monochrome and filtered images, representative regions of interest of different sizes were selected for each desired class. Then, the mean reflectance values of these regions were treated as training samples and were manually labelled in three semantic classes: soil, vegetation and others elements of the scene. With the obtained set of 30 samples per class, the SVMs of the proposed algorithm were trained to classify the pixels of the images. The third phase of the experimental stage was carried out with the aim of finding out a relationship between the data acquired with the SWIR camera and the measurements obtained with the penetrometer. Datasets included LWIR and SWIR images acquired in scenarios with dry and wet soil. Moreover, in each of the conducted experiments, several measures were also carried out with a penetrometer, so that the soil penetration resistance information can be associated with the corresponding mean reflectance value of the SWIR image acquired with the sensory rig, and in this way, be subsequently used for training the algorithms responsible of estimating the ground bearing capacity. An example of the data obtained in this phase is presented on From the results obtained in all the tests carried out on this third phase it is possible to confirm that the mean reflectance values of the areas of interest belonging to the soil on normalised SWIR images decrease with increasing soil moisture, as it was expected, since water strongly absorbs light in the SWIR wavelengths. Thermal images also confirm a decrease in the temperature through the day when the ground is wet, in comparison with the dry soil. However, as we stated in the previous section, the direct utilisation of thermal information is discarded for the estimation of the ground bearing capacity, since there are several factors that influence soil temperature, including angle to the sun, weather conditions and the time of day. Even so, thermal information is very useful for other intermediate processing steps that help us to identify areas that are considerably wetter than the rest, providing automatic delineation of areas for the calculation of the mean reflectance values on normalised SWIR images. On the other hand, penetrometer measurements reinforce the idea that the penetration resistance, and therefore, the bearing capacity, decrease when the soil is moist. Taking into account these results, a linear regression approach is applied for modelling the relationship between the soil penetration resistance (at a depth of 0.1 m) and the corresponding mean reflectance value of this region of interest on a normalised SWIR image. Final set of experiments were intended to evaluate the whole system proposed for the estimation of the ground bearing capacity. Acquired datasets included LWIR thermal, SWIR and monochrome images, as well as two filtered images acquired with band-pass filters that have centre wavelengths of 624 nm and 950 nm. Results of these experiments are described in next Section.In order to validate the capabilities of the proposed system, a total of 10 scenes with variable soil water content conditions were acquired, processed and evaluated. Ground truth data was carefully collected with a penetrometer for each scene in order to carry out a quantitative assessment of the proposed solution. The pixel-based classification map resulting from applying the multi-class SVM classifier to the images presented in Next, the K-means clustering is applied to the pixels of the LWIR thermal image that remain after conducting the masking, with the aim of partitioning these pixels into four exclusive clusters. Every pixel in the image is labelled in accordance with the cluster index assigned by the K-means procedure. Cluster corresponding to white pixels is disregarded, since it does not belong to the soil class. The comparison of the absolute differences obtained from the different clusters with certain predefined values, will help us to determine if the soil sample has a homogeneous bearing capacity, or if on the contrary, the soil is heterogeneous, exhibiting areas with significantly different bearing capacities . This last case is clearly observed in Then, taking into account these estimations, the model obtained from the linear regression presented in Next, LWIR thermal images acquired during the experimental tests demonstrated that under nominal weather conditions, wet soil is cooler than dry soil through the day. At both high and low temperatures, there was still a notably thermal difference between the dry and wet soil. For that reason, K-means clustering applied to the masked thermal images resulted to be a suitable option to delimit areas with different temperatures, and consequently, areas that are candidates to present different water content. However, temperature values are disregarded for the direct estimation of the ground bearing capacity due to its high variability depending on several external factors. Results from the clustering procedure are also applied to the normalized SWIR images. This is a great advantage, since although mean reflectance values on normalized SWIR images diminish with increasing soil water content, differences in a heterogeneous terrain can be difficult to detect. On the other hand, the multi-class SVM classifier applied to the VIS-NIR images also demonstrated to be very useful for discriminating soil from other elements of the scene. Discarding those pixels that belong to other elements of the scene different from soil contributes to reduce estimation errors during the calculation of the mean reflectance values from the SWIR images. Despite of the successful performance of the classifier, a closer observation of the results shown in Gathering the results obtained from the 10 evaluated scenes, it is possible to highlight that the proposed approach showed a capability of 79.3% for predicting correctly the ground bearing capacity from the mean reflectance value of the normalized SWIR images, with a mean prediction error of 20.7%. Despite the high level of correctness attained by the proposed solution when it comes to estimating the ground bearing capacity, it should be taken into account that the prediction model obtained from the linear regression mainly relies on experimental data. Therefore, in order to guarantee a robust estimation of the ground bearing capacity, data acquisition should be extended to different types of terrains. Even, it could be desired to count with one predictive model for each type of terrain.The presented approach can be also very useful for helping decision making systems in selecting those areas that tend to minimize ground damage during mechanical operation with wheeled mobile robots or vehicles.This paper proposes a multisensory system based on VIS-NIR, SWIR and LWIR imagery and a sequential algorithm that combines a registration procedure, a multi-class SVM classifier, a K-means clustering and a linear regression for estimating the ground bearing capacity. The presented solution can be utilized in natural scenarios, in real-time and in a non-destructive manner. Therefore, the sensory rig and the cited algorithm can be installed on-board a mobile robot or vehicle for estimating ground conditions before traversing the field, avoiding disturbances of the site and significantly reducing ground damages.An extensive experimental campaign was carried out not only for acquiring the data required for the design phase but also for assessing the capabilities of the proposed approach. Experimental results have shown that both the multisensory system and the sequential algorithm exhibit a satisfactory performance. Comparison of ground bearing capacity estimations resulting from the experimental tests with the ground truth data obtained with the penetrometer shows the feasibility and the potential of the proposed method. Future work should be directed to enhancing the prediction model obtained from the linear regression. For that, a more widespread experimental study should be conducted in order to include different types of soils that contribute to guarantee a robust estimation of the ground bearing capacity.