  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.
