Abstract
Landing site selection is one of the most important tasks in autonomous landing of unmanned aerial vehicles (UAVs). In this paper, a new method to autonomously select safe landing sites from LiDAR point clouds is proposed. Principal component analysis CPCA) and an improved region growing algorithm are utilized to detect flat regions, where plane fitting is executed afterwards for terrain complexity assessment using an improved progressive sample consensus (PROSAC) algorithm. The most suitable landing site of a landing zone is selected according to the terrain complexity. Terrain point clouds of urban and natural scenes are used for simulation experiments. Experimental results show that the landing zones can be classified successfully and the selected landing sitescan meet the safety requirements, which demonstrate the effectiveness and feasibility of our proposed method.
Published Version
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