UAS TOPOGRAPHIC MAPPING WITH VELODYNE LiDAR SENSOR

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

MONITORING OF FLUVIAL TRANSPORT IN THE MOUNTAIN RIVER BED USING TERRESTRIAL LASER SCANNING

The fluvial transport is the surface process that has a strong impact on the topography changes, especially in mountain areas. Traditional hydrological measurements usually give a good understanding of the river flow, however, the information of the bedload movement in the rivers is still insufficient. In particular, there is limited knowledge about the movement of the largest clasts, i.e. boulders. This investigation addresses mentioned issues by employing Terrestrial Laser Scanning (TLS) to monitor annual changes of the mountain river bed. The vertical changes were estimated based on the Digital Elevation Model (DEM) of difference (DoD) while transported boulders were identified based on the distances between point clouds and RGB-coloured points. Combined RGB point clouds allowed also to measure 3D displacements of boulders. The results showed that the highest dynamic of the fluvial process occurred between years 2012-2013. Obtained DoD clearly indicated alternating zones of erosion and deposition of the sediment finer fractions in the local sedimentary traps. The horizontal displacement of the rock material in the river bed showed high complexity resulting in the displacement of large boulders (major axis about 0.8 m) for the distance up to 2.3 m

DETERMINING GEOMETRIC PARAMETERS OF AGRICULTURAL TREES FROM LASER SCANNING DATA OBTAINED WITH UNMANNED AERIAL VEHICLE

The estimation of dendrometric parameters has become an important issue for agriculture planning and for the efficient management of orchards. Airborne Laser Scanning (ALS) data is widely used in forestry and many algorithms for automatic estimation of dendrometric parameters of individual forest trees were developed. Unfortunately, due to significant differences between forest and fruit trees, some contradictions exist against adopting the achievements of forestry science to agricultural studies indiscriminately. In this study we present the methodology to identify individual trees in apple orchard and estimate heights of individual trees, using high-density LiDAR data (3200 points/m2) obtained with Unmanned Aerial Vehicle (UAV) equipped with Velodyne HDL32-E sensor. The processing strategy combines the alpha-shape algorithm, principal component analysis (PCA) and detection of local minima. The alpha-shape algorithm is used to separate tree rows. In order to separate trees in a single row, we detect local minima on the canopy profile and slice polygons from alpha-shape results. We successfully separated 92 % of trees in the test area. 6 % of trees in orchard were not separated from each other and 2 % were sliced into two polygons. The RMSE of tree heights determined from the point clouds compared to field measurements was equal to 0.09 m, and the correlation coefficient was equal to 0.96. The results confirm the usefulness of LiDAR data from UAV platform in orchard inventory

PERFORMANCE EVALUATION OF sUAS EQUIPPED WITH VELODYNE HDL-32E LiDAR SENSOR

The Velodyne HDL-32E laser scanner is used more frequently as main mapping sensor in small commercial UASs. However, there is still little information about the actual accuracy of point clouds collected with such UASs. This work evaluates empirically the accuracy of the point cloud collected with such UAS. Accuracy assessment was conducted in four aspects: impact of sensors on theoretical point cloud accuracy, trajectory reconstruction quality, and internal and absolute point cloud accuracies. Theoretical point cloud accuracy was evaluated by calculating 3D position error knowing errors of used sensors. The quality of trajectory reconstruction was assessed by comparing position and attitude differences from forward and reverse EKF solution. Internal and absolute accuracies were evaluated by fitting planes to 8 point cloud samples extracted for planar surfaces. In addition, the absolute accuracy was also determined by calculating point 3D distances between LiDAR UAS and reference TLS point clouds. Test data consisted of point clouds collected in two separate flights performed over the same area. Executed experiments showed that in tested UAS, the trajectory reconstruction, especially attitude, has significant impact on point cloud accuracy. Estimated absolute accuracy of point clouds collected during both test flights was better than 10 cm, thus investigated UAS fits mapping-grade category

MONITORING AIRCRAFT MOTION AT AIRPORTS BY LIDAR

Improving sensor performance, combined with better affordability, provides better object space observability, resulting in new applications. Remote sensing systems are primarily concerned with acquiring data of the static components of our environment, such as the topographic surface of the earth, transportation infrastructure, city models, etc. Observing the dynamic component of the object space is still rather rare in the geospatial application field; vehicle extraction and traffic flow monitoring are a few examples of using remote sensing to detect and model moving objects. Deploying a network of inexpensive LiDAR sensors along taxiways and runways can provide both geometrically and temporally rich geospatial data that aircraft body can be extracted from the point cloud, and then, based on consecutive point clouds motion parameters can be estimated. Acquiring accurate aircraft trajectory data is essential to improve aviation safety at airports. This paper reports about the initial experiences obtained by using a network of four Velodyne VLP- 16 sensors to acquire data along a runway segment

Multi-UAV Carrier Phase Differential GPS and Vision-based Sensing for High Accuracy Attitude Estimation

This paper presents a cooperative navigation technique which exploits relative vision-based sensing and carrier-phase differential GPS (CDGPS) among antennas embarked on different flying platforms, to provide accurate UAV attitude estimates in real time or in post-processing phase. It is assumed that all UAVs are under nominal GPS coverage. The logical architecture and the main algorithmic steps are highlighted, and the adopted CDGPS processing strategy is described. The experimental setup used to evaluate the proposed approach comprises two multi-rotors and two ground antennas, one of which is used as a benchmark for attitude accuracy estimation. Results from flight tests are presented in which the attitude solution obtained by integrating CDGPS and vision (CDGPS/Vision) measurements within and Extended Kalman Filter is compared with estimates provided by the onboard navigation system and with the results of a formerly developed code-based differential GPS (DGPS/Vision) approach. Benchmark-based analyses confirm that CDGPS/Vision approach outperforms both onboard navigation system and DGPS/Vision approach