Localization and Mapping for UGV in Dynamic Scenes with Dynamic Objects Eliminated

• Published in: Machines (Basel) Vol. 10; no. 11; p. 1044
• Main Authors: Li, Junsong, He, Jilin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Accuracy; Algorithms; Artificial satellites; Automobiles; Clustering; Computational geometry; Computational linguistics; Convexity; dynamic scene; Evaluation; Feature extraction; Global navigation satellite system; Industrial parks; Kalman filters; Language processing; Lidar; Methods; Natural language interfaces; Neural networks; Object recognition; odometry; Optical radar; Position errors; Remote sensing; Robot dynamics; Robots; Semantics; Sensors; Simultaneous localization and mapping; SLAM; Unmanned ground vehicles; Vehicles
• ISSN: 2075-1702; 2075-1702
• DOI: 10.3390/machines10111044

SLAM (Simultaneous Localization and Mapping) based on lidar is an important method for UGV (Unmanned Ground Vehicle) localization in real time under GNSS (Global Navigation Satellite System)-denied situations. However, dynamic objects in real-world scenarios affect odometry in SLAM and reduce localization accuracy. We propose a novel lidar SLAM algorithm based on LOAM (Lidar Odometry and Mapping), which is popular in this field. First, we applied elevation maps to label the ground point cloud. Then we extracted convex hulls in point clouds based on scanlines as materials for dynamic object clustering. We replaced these dynamic objects with background point cloud to avoid accuracy reduction. Finally, we extracted feature points from ground points and non-ground points, respectively, and matched these feature points frame-to-frame to estimate ground robot motion. We evaluated the proposed algorithm in dynamic industrial park roads, and it kept UGV maximum relative position error less than 3% and average relative position error less than 2%.