TY - GEN
T1 - Air-Ground Collaborative Mapping Based on Region Matching Under Terrain Constraints
AU - Pei, Shuo
AU - Zheng, Xin
AU - Jiang, Xiangdong
AU - Li, Zheng
AU - Yao, Weiran
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper proposes an air-ground collaborative point cloud map fusion and construction framework based on Lidar Odometry And Mapping and Normal Distribution Transform matching, which can still operate normally under low illumination and Global Navigation Satellite System denied conditions. To deal with the localization problem between agents in the absence of initial pose information, this paper designs a matching mechanism based on the front-end and back-end structure. The front-end performs rough matching of the original point cloud, and the back-end achieves fine matching of the map point cloud. A filtering mechanism for spatial overlapping maps is investigated to obtain accurate pose transformation between agents. To demonstrate the feasibility of the design scheme, simulations are conducted in the gazebo environment. The result shows that the error between initial relative pose obtained from mapping and the real setting is under 1.6%.
AB - This paper proposes an air-ground collaborative point cloud map fusion and construction framework based on Lidar Odometry And Mapping and Normal Distribution Transform matching, which can still operate normally under low illumination and Global Navigation Satellite System denied conditions. To deal with the localization problem between agents in the absence of initial pose information, this paper designs a matching mechanism based on the front-end and back-end structure. The front-end performs rough matching of the original point cloud, and the back-end achieves fine matching of the map point cloud. A filtering mechanism for spatial overlapping maps is investigated to obtain accurate pose transformation between agents. To demonstrate the feasibility of the design scheme, simulations are conducted in the gazebo environment. The result shows that the error between initial relative pose obtained from mapping and the real setting is under 1.6%.
KW - LiDAR SLAM
KW - collaborative mapping
KW - map fusion
KW - posture opti-mization
UR - https://www.scopus.com/pages/publications/85180125129
U2 - 10.1109/ICUS58632.2023.10318453
DO - 10.1109/ICUS58632.2023.10318453
M3 - 会议稿件
AN - SCOPUS:85180125129
T3 - Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023
SP - 1399
EP - 1404
BT - Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023
A2 - Song, Rong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Unmanned Systems, ICUS 2023
Y2 - 13 October 2023 through 15 October 2023
ER -