Multi-session Map Construction in Outdoor Dynamic Environment
This addresses map construction for robots in outdoor environments, but it appears incremental as it builds on existing multi-session and dynamic detection techniques.
The paper tackles the problem of constructing maps in large-scale outdoor dynamic environments by merging multiple sessions of 3D LiDAR data to distinguish low dynamics, and the experimental results with a VLP-16 Velodyne LiDAR prove the method's validity and robustness.
Map construction in large scale outdoor environment is of importance for robots to robustly fulfill their tasks. Massive sessions of data should be merged to distinguish low dynamics in the map, which otherwise might debase the performance of localization and navigation algorithms. In this paper we propose a method for multi-session map construction in large scale outdoor environment using 3D LiDAR. To efficiently align the maps from different sessions, a laser-based loop closure detection method is integrated and the sequential information within the submaps is utilized for higher robustness. Furthermore, a dynamic detection method is proposed to detect dynamics in the overlapping areas among sessions of maps. We test the method in the real-world environment with a VLP-16 Velodyne LiDAR and the experimental results prove the validity and robustness of the proposed method.