A Fast Multiplane Segmentation Algorithm for Sparse 3-D LiDAR Point Clouds by Line Segment Grouping
Xiaoguo Du, Yuchu Lu, Qijun Chen
Abstract
This article describes an approach for extracting multiple planar regions in 3-D point clouds from spinning multibeam LiDARs. This technique benefits from the intrinsic structure of LiDARs and projective geometry, which allows us to extract line segments efficiently in 2-D space and then cluster those line segments to form planes. To extract planes from line primitives, we introduce a novel line segment grouping approach by alternatively searching candidate plane seeds of adjacent line segments and breadth-first searching for neighboring lines fallen on the seeded plane. Exhaustive experiments have been conducted with simulation, realistic data, and a public plane segmentation evaluation benchmark. Experimental results show that our method works well on sparse point clouds with the fastest running speed compared to state-of-the-art methods.