Efficient LiDAR Odometry for Autonomous Driving

TL;DR

This paper introduces an efficient LiDAR odometry method combining spherical range images and bird's-eye-view maps, improving ground point handling and model update speed for autonomous navigation.

Contribution

It proposes a novel approach that integrates non-ground spherical range images with bird's-eye-view maps and a range adaptive normal estimation for improved LiDAR odometry.

Findings

Achieves promising results on KITTI benchmark

Handles ground points more effectively

Offers a fast, memory-efficient model update scheme

Abstract

LiDAR odometry plays an important role in self-localization and mapping for autonomous navigation, which is usually treated as a scan registration problem. Although having achieved promising performance on KITTI odometry benchmark, the conventional searching tree-based approach still has the difficulty in dealing with the large scale point cloud efficiently. The recent spherical range image-based method enjoys the merits of fast nearest neighbor search by spherical mapping. However, it is not very effective to deal with the ground points nearly parallel to LiDAR beams. To address these issues, we propose a novel efficient LiDAR odometry approach by taking advantage of both non-ground spherical range image and bird's-eye-view map for ground points. Moreover, a range adaptive method is introduced to robustly estimate the local surface normal. Additionally, a very fast and memory-efficient model update scheme is proposed to fuse the points and their corresponding normals at different time-stamps. We have conducted extensive experiments on KITTI odometry benchmark, whose promising results demonstrate that our proposed approach is effective.