  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.
