Autonomous UAVs landing site selection from point cloud in unknown environments

Autonomous safe landing of UAVs is an important and challenging task in unknown environments, as almost no prior scene information can be leveraged for navigation. Most existing methods cannot address this issue completely, due to terrain uncertainty and system complexity. In this paper, we present a novel and complete framework for UAVs landing, which is built on point cloud in coarse-to-fine manner. Besides, our framework is designed with modularity and has four modules: point cloud preprocessing, coarse landing site selection, fine terrain evaluation, and landing optimal model. Specifically, a composite preprocessing scheme is applied to simultaneously filter noise, generate 3D Octo-map and plan the path on the raw point cloud. To balance the accuracy and real-time of the landing system, only promising coarse landing locations are automatically selected by adopting the proposed multi-stage process in grid-map. Based on the result of coarse stage, a fine-grained 3D validation is modeled by multiple terrain factors, which can further improve landing safety. Finally, a novel landing optimal model fuses terrain condition, fuel consumption, and second landing validation to determine the final landing sites during descent. Extensive experiments have been successfully conducted on different real-world and unknown environments, verifying that our method can select safe landing sites for UAVs robustly. Additionally, the system is further evaluated under normal, emergency, and rescue situations respectively to highlight different landing requirements.