Fast approximation for geometric classification of LiDAR returns

Current LiDAR classification methods are excessively slow to be used in real-time navigation systems, even though they are useful for human perception. These methods typically analyze curvature by applying Principal Component Analysis (PCA) to each point in a point cloud. For variable-density aerial LiDAR obtained by at a shallow angle with respect to the ground rather than in a top-down fashion, the variations in density pose special challenges in terms of choosing the appropriate PCA parameters. In this paper we use gridded approximate nearest neighbor searches for fast classification of geometric features in large LiDAR point clouds. The underlying algorithm exploits spatial hashes and the forgiving nature of PCA as a part of geometric classification. We show a factor of 10-20 speed up for both actual and simulated point clouds with little or no loss in classification performance. Our approach is applicable to both uniform and variable-density aerial LiDAR datasets.