A Hierarchical LiDAR Odometry via Maximum Likelihood Estimation With Tightly Associated Distributions

Abstract

LiDAR odometry has gained popularity due to accurate depth measurement with the robustness to illuminations. However, existing distribution-based methods do not sufficiently exploit the information from source point cloud, which affects the odometry performance. In this paper, a novel distribution-to-distribution matching method is proposed based on maximum likelihood estimation to solve relative transformation, where source and target point sets are tightly jointed to represent the sampling distribution in the objective function. On this basis, a hierarchical 3D LiDAR odometry with the low-level scan-to-map matching and high-level fixed-lag smoothing is designed. With the decoupling strategy, the matching method is extended to a fixed-lag smoothing module and the heavy computation burden is overcome. Our smoothing module is universal, which can be attached to LiDAR odometry framework for performance improvement. The experiments on KITTI dataset, Newer College dataset, and large-scale KITTI-360 dataset verify the effectiveness of the proposed method.