LiTAMIN: LiDAR-based Tracking And Mapping by Stabilized ICP for Geometry Approximation with Normal Distributions

Abstract

This paper proposes a 3D LiDAR simultaneous localization and mapping (SLAM) method that improves accuracy, robustness, and computational efficiency for an iterative closest point (ICP) algorithm employing a locally approximated geometry with clusters of normal distributions. In comparison with previous normal distribution-based ICP methods, such as normal distribution transformation and generalized ICP, our ICP method is simply stabilized with normalization of the cost function by the Frobenius norm and a regularized covariance matrix. The previous methods are stabilized with principal component analysis, whose computational cost is higher than that of our method. Moreover, our SLAM method can reduce the effect of incorrect loop closure constraints. The experimental results show that our SLAM method has advantages over open source state-of-the-art methods, including LOAM, LeGO-LOAM, and hdl_graph_slam.