Performance evaluation of suas equipped with Velodyne HDL-32E LiDAR sensor

Abstract

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.