  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.
