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In this paper, a three-dimensional light detection and ranging simultaneouslocalization and mapping (SLAM) method is proposed that is available fortracking and mapping with 500--1000 Hz processing. The proposed methodsignificantly reduces the number of points used for point cloud registrationusing a novel ICP metric to speed up the registration process while maintainingaccuracy. Point cloud registration with ICP is less accurate when the number ofpoints is reduced because ICP basically minimizes the distance between points.To avoid this problem, symmetric KL-divergence is introduced to the ICP costthat reflects the difference between two probabilistic distributions. The costincludes not only the distance between points but also differences betweendistribution shapes. The experimental results on the KITTI dataset indicatethat the proposed method has high computational efficiency, stronglyoutperforms other methods, and has similar accuracy to the state-of-the-artSLAM method.

LiTAMIN2: Ultra Light LiDAR-based SLAM using Geometric Approximation applied with KL-Divergence