UAS TOPOGRAPHIC MAPPING WITH VELODYNE LiDAR SENSOR

Abstract

Commission I, ICWG I/VbKEY WORDS: UAS, UAV, LiDAR, mapping, performance analysis ABSTRACT:Unmanned Aerial System (UAS) technology is nowadays willingly used in small area topographic mapping due to low costs and good quality of derived products. Since cameras typically used with UAS have some limitations, e.g. cannot penetrate the vegetation, LiDAR sensors are increasingly getting attention in UAS mapping. Sensor developments reached the point when their costs and size suit the UAS platform, though, LiDAR UAS is still an emerging technology. One issue related to using LiDAR sensors on UAS is the limited performance of the navigation sensors used on UAS platforms. Therefore, various hardware and software solutions are investigated to increase the quality of UAS LiDAR point clouds. This work analyses several aspects of the UAS LiDAR point cloud generation performance based on UAS flights conducted with the Velodyne laser scanner and cameras. The attention was primarily paid to the trajectory reconstruction performance that is essential for accurate point cloud georeferencing. Since the navigation sensors, especially Inertial Measurement Units (IMUs), may not be of sufficient performance, the estimated camera poses could allow to increase the robustness of the estimated trajectory, and subsequently, the accuracy of the point cloud. The accuracy of the final UAS LiDAR point cloud was evaluated on the basis of the generated DSM, including comparison with point clouds obtained from dense image matching. The results showed the need for more investigation on MEMS IMU sensors used for UAS trajectory reconstruction. The accuracy of the UAS LiDAR point cloud, though lower than for point cloud obtained from images, may be still sufficient for certain mapping applications where the optical imagery is not useful.

Geometric Targets for UAS Lidar

“…Often, only the elevation errors of points or derived raster digital elevation model (DEM) cells in the neighborhood of a reference surface are used, neglecting any measure of horizontal errors. This is the case for both conventional occupied-platform lidar and UAS lidar [6].…”

Identification of Water Body Extent Based on Remote Sensing Data Collected with Unmanned Aerial Vehicle

“…Laser scanners, such as Velodyne, produce the point cloud in a scanner local coordinate system that changes its global position and orientation during the flight. In order to add appropriate georeferencing to this point cloud (transformation from local to global coordinate system, e.g., Reference [36]), the trajectory of UAV in 6 degrees of freedom (position and orientation) needs to be reconstructed. This task is solved by integrating an on-board rover and a ground base GNSS station data with an on-board inertial data (linear accelerations and angular velocities) using an Extended Kalman Filter [37].…”

“…This task is solved by integrating an on-board rover and a ground base GNSS station data with an on-board inertial data (linear accelerations and angular velocities) using an Extended Kalman Filter [37]. The use of high-grade on-board navigation sensors, especially the IMU (Inertial Measurement Unit), has a critical impact on the accuracy of the reconstructed trajectory and, consequently, on the accuracy of the created point cloud [36]. In this work, the georeferenced LiDAR point cloud was obtained using vendor provided software (NovAtel Inertial Explorer for trajectory reconstruction and Leica Pegasus AutoP for point cloud coordinate transformations).…”

Reduction Method for Mobile Laser Scanning Data

“…To use the point cloud for option 2, the data should be georeferenced. Standard georeferencing of MLS data was based on the transformation from the scanner local coordinates to global coordinates using boresight parameters and navigation information from the on-board GPS and IMU [6,22].…”