A vertical constraint enhanced method for indoor LiDAR SLAM algorithms

Zhennan Tian; Ningbo Bu; Jiangjian Xiao; Gen Xu; Haolei Ru; Jianfei Ge

doi:10.1117/12.2662203

28 December 2022 A vertical constraint enhanced method for indoor LiDAR SLAM algorithms

Zhennan Tian, Ningbo Bu, Jiangjian Xiao, Gen Xu, Haolei Ru, Jianfei Ge

Author Affiliations +

Proceedings Volume 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022); 125060G (2022) https://doi.org/10.1117/12.2662203
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2022), 2022, Beijing, China

Abstract

LiDAR-based SLAM Systems are widely applied in robotics for their accuracy and robustness. However, accurate localization in small-scale indoor scenes is challenging since the point cloud features of the sparse line scan LiDAR cannot always provide sufficient space constraints. In our experiments, even state-of-the-art methods have heavy odometry drift. In this paper, to address this problem, we propose a method that can improve the performance of existing algorithms in small-scale indoor scenes. By installing a LiDAR perpendicular to the existing LiDAR, our method enhances the constraint in the vertical direction. We test our method on LOAM and FAST-LIO2, and the results show significant improvements on our own collected datasets. In addition, to accurately merge the two LiDAR’s cloud points, we propose a high-accuracy dual-LiDAR calibration method with rotation and translation errors less than 0.005 rad and 0.01 m respectively.

Citation Download Citation

Zhennan Tian, Ningbo Bu, Jiangjian Xiao, Gen Xu, Haolei Ru, and Jianfei Ge "A vertical constraint enhanced method for indoor LiDAR SLAM algorithms", Proc. SPIE 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022), 125060G (28 December 2022); https://doi.org/10.1117/12.2662203

ACCESS THE FULL ARTICLE

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY