Multi-session Map Construction in Outdoor Dynamic Environment

TL;DR

This paper presents a robust method for multi-session 3D LiDAR map construction in outdoor environments, integrating loop closure detection and dynamic object filtering to improve localization and navigation.

Contribution

It introduces a novel multi-session map construction approach that combines laser-based loop closure detection with dynamic object filtering for outdoor environments.

Findings

Effective map alignment across sessions demonstrated

Dynamic detection improves map accuracy

Method validated with real-world LiDAR data

Abstract

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.