Study on the influence of airborne LiDAR measurement data representation method on DRL-based UAV navigation performance

Abstract With the development of unmanned aerial vehicle (UAV) technology, UAV navigation based on deep reinforcement learning (DRL) has become a current research focus. In the existing research on UAV navigation based on LiDAR, most of them construct the state space of DRL using the direct measurement data of LiDAR, ignoring the impact of the representation method of LiDAR measurement data on navigation performance. To address this issue, this study analyzed the characteristics of different representation methods of LiDAR measurement data. Considering the impact of UAV angle changes, the LiDAR measurement data were represented by polar coordinates and Cartesian coordinates respectively to construct the state space of the UAV navigation model. Based on two classic DRL frameworks, through a large number of flight tests in complex static and dynamic scenarios, it was found that when considering the dynamic information of the environment, the models based on polar coordinates and Cartesian coordinates have better navigation performance, which provides new ideas for the effective utilization of LiDAR perception information.