  Selecting safe landing sites in non-cooperative environments is a key step
towards the full autonomy of UAVs. However, the existing methods have the
common problems of poor generalization ability and robustness. Their
performance in unknown environments is significantly degraded and the error
cannot be self-detected and corrected. In this paper, we construct a UAV system
equipped with low-cost LiDAR and binocular cameras to realize autonomous
landing in non-cooperative environments by detecting the flat and safe ground
area. Taking advantage of the non-repetitive scanning and high FOV coverage
characteristics of LiDAR, we come up with a dynamic time depth completion
algorithm. In conjunction with the proposed self-evaluation method of the depth
map, our model can dynamically select the LiDAR accumulation time at the
inference phase to ensure an accurate prediction result. Based on the depth
map, the high-level terrain information such as slope, roughness, and the size
of the safe area are derived. We have conducted extensive autonomous landing
experiments in a variety of familiar or completely unknown environments,
verifying that our model can adaptively balance the accuracy and speed, and the
UAV can robustly select a safe landing site.
