An Adaptive Algorithm for Embedded Real-Time Point Cloud Ground Segmentation

This paper presents a fast algorithm for ground segmentation that quickly and accurately differentiates ground points from obstacles after processing unstructured point clouds. Unlike most recent approaches found in the literature, it does not rely on any sensor-specific feature or data ordering. It performs an orthogonal projection into the horizontal plane followed by a top-down 4-ary tree segmentation. The segmentation self-adapts to the point cloud, focusing processing effort on detailed areas. This adaptive subdivision process allows successfully extracting ground points even when the floor is not perfectly flat. Finally, tests demonstrate real-time performance for execution in low cost embedded devices.