Efficient LiDAR Odometry for Autonomous Driving

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 the KITTI odometry benchmark, the conventional tree-based neighbor search 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 images and bird's-eye-view maps 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 the KITTI odometry benchmark and UrbanLoco dataset, whose promising results demonstrate that our proposed approach is effective.