Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance

Seungcheol Park, Shuyu Wang, Hunjung Lim, U Kang

Abstract: Given 3D LiDAR point clouds, how can we segment them fast and accurately? Fast and accurate segmentation of 3D LiDAR points is an important issue in mobile robotics with various applications in classification, tracking, SLAM, etc. Despite its importance, existing methods do not provide both speed and accuracy; in particular, methods performing segmentation in the 3D domain are too slow, disabling its use in real-time processing. In this paper, we propose Curved-Voxel Clustering (CVC), a fast and accurate method for segmenting 3D LiDAR point clouds utilizing LiDAR-optimized curved-voxel. CVC attains fine discriminations by considering three important aspects for clustering 3D LiDAR points: distance from the sensor, directional resolutions, and rarity of points. CVC succeeds in providing real-time performance by carefully managing curved-voxels with a hash table. Especially, CVC works well on sparse 3D point clouds. Through experiments, we show that our method is up to 1.7× faster and 30% more accurate than other segmentation methods. CVC enables real-time segmentation with more than 20 runs in a second.