LiTAMIN2: Ultra Light LiDAR-based SLAM using Geometric Approximation applied with KL-Divergence
This addresses computational bottlenecks for real-time SLAM applications in robotics or autonomous vehicles, though it is incremental as it builds on existing ICP methods.
The paper tackles the problem of slow point cloud registration in LiDAR-based SLAM by reducing the number of points used, achieving processing at 500-1000 Hz while maintaining accuracy similar to state-of-the-art methods on the KITTI dataset.
In this paper, a three-dimensional light detection and ranging simultaneous localization and mapping (SLAM) method is proposed that is available for tracking and mapping with 500--1000 Hz processing. The proposed method significantly reduces the number of points used for point cloud registration using a novel ICP metric to speed up the registration process while maintaining accuracy. Point cloud registration with ICP is less accurate when the number of points is reduced because ICP basically minimizes the distance between points. To avoid this problem, symmetric KL-divergence is introduced to the ICP cost that reflects the difference between two probabilistic distributions. The cost includes not only the distance between points but also differences between distribution shapes. The experimental results on the KITTI dataset indicate that the proposed method has high computational efficiency, strongly outperforms other methods, and has similar accuracy to the state-of-the-art SLAM method.