CurvedVoxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with RealTime Performance 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 realtime processing. In this paper, we propose CurvedVoxel Clustering (CVC), a fast and accurate method for segmenting 3D LiDAR point clouds utilizing LiDARoptimized curvedvoxel. 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 realtime performance by carefully managing curvedvoxels with a hash table. Especially, CVC works well on sparse 3D point clouds. Through experiments, we show that our method is up to 1.7xc3x97 faster and 30% more accurate than other segmentation methods. CVC enables realtime segmentation with more than 20 runs in a second.

Unsupervised Segmentation Of Urban 3D Point Cloud Based On Lidarimage As one of the most vital fields in 3D scene understanding, segmentation of lidar point cloud has verity of applications such as unmanned vehicle. In order to deal with the problem of limited local computing resources, this paper proposes a twostep unsupervised learningfree method to segment sparse 3D point cloud with low computational demands. The 3D point cloud data is projected into a lidarspecific 2D coordinate system called lidarimage. For unmanned vehicles, road is a special kind of object, so the algorithm divides road firstly by using improved lidarhistogram. The algorithm further examines the geometric connection between adjacent points in 3D space, thus dividing nonroad points into independent subsets. Experiments on KITTI dataset validate the proposed algorithm outperforms similar methods.