A vertical constraint enhanced method for indoor LiDAR SLAM algorithms

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.