Perception-aware Receding Horizon Path Planning for UAVs with LiDAR-based SLAM

Abstract

This paper presents a perception-aware path planning framework for unmanned aerial vehicles (UAVs) that explicitly considers perception quality of a light detection and ranging (LiDAR) sensor. The perception quality is quantified based on how scattered feature points are in LiDAR-based simultaneous localization and mapping, which can improve the accuracy of pose estimation of UAVs. In the planning step of a UAV, the proposed framework selects the best path based on the perception quality from a library of candidate paths generated by the rapidly-exploring random trees algorithm. Consequently, the UAV can autonomously fly to a destination in a receding horizon manner. Several simulation trials of the photorealistic environments confirm that our proposed path planner reduces pose estimation error by approximately 85 % on average as compared with a purely-reactive path planner.