Long-Arm Three-Dimensional LiDAR for Antiocclusion and Antisparsity Point Clouds

Abstract

Light detection and ranging (LiDAR) systems, also called laser radars, have a wide range of applications. This article considers two problems in LiDAR data. The first problem is occlusion. A LiDAR acquires point clouds by scanning the surrounding environment with laser beams emitting from its center, and therefore an object behind another cannot be scanned. The second problem is sample sparsity. LiDAR scanning is usually taken with a fixed angular step; consequently, the sample points on an object surface at a long distance are sparse and thus accurate boundary and detailed surface of the object cannot be obtained. To address the occlusion problem, we design and implement a novel 3-D LiDAR with a long arm, which is able to scan occluded objects from their flanks. To address the sample-sparsity problem, we propose an adaptive resolution scanning method that detects the object and adjusts the angular step in real time according to the density of points on the object being scanned. Experiments on our prototype system and scanning method verify their effectiveness in antiocclusion and antisparsity as well as accuracy in measuring. The data and the codes are shared on the web site of https://github.com/SCVision/LongarmLiDAR .