关键词: 无人机, 自主择址；数据融合；地形成本图；语义分割

Abstract: As a new type of aircraft, the unmanned aerial vehicle (UAV) is gradually integrating into the modern weaponry system and becoming an indispensable and important part of the military field. In order to equip UAVs with a safe landing decision-making system that can autonomously perform landing tasks without ground marking, this paper proposes a phased autonomous address selection technique based on multi-sensor data fusion from coarse to fine. Based on the semantic segmentation of the image information to realize the rough landing point search, after guiding the UAV to reduce the flight altitude, the terrain parameters are calculated based on the elevation value of the point cloud information to construct the terrain cost map, and the semantic information of the image is fused considering the category of the terrain to complete the fine landing point search. The experimental results show that this technology can well delineate the safe and dangerous areas, and enable the UAV to autonomously obtain a safe landing position. Meanwhile, comparative analysis of the decision-making in the fine landing point search stage with the fitted point cloud plane verifies that the technique can save decision-making time to a greater extent and improve the efficiency of site selection.

Key words: unmanned aerial vehicle, autonomous landing site selection, data fusion, terrain cost map, semantic segmentation