  The correct ego-motion estimation basically relies on the understanding of
correspondences between adjacent LiDAR scans. However, given the complex
scenarios and the low-resolution LiDAR, finding reliable structures for
identifying correspondences can be challenging. In this paper, we delve into
structure reliability for accurate self-supervised ego-motion estimation and
aim to alleviate the influence of unreliable structures in training, inference
and mapping phases. We improve the self-supervised LiDAR odometry substantially
from three aspects: 1) A two-stage odometry estimation network is developed,
where we obtain the ego-motion by estimating a set of sub-region
transformations and averaging them with a motion voting mechanism, to encourage
the network focusing on representative structures. 2) The inherent alignment
errors, which cannot be eliminated via ego-motion optimization, are
down-weighted in losses based on the 3D point covariance estimations. 3) The
discovered representative structures and learned point covariances are
incorporated in the mapping module to improve the robustness of map
construction. Our two-frame odometry outperforms the previous state of the arts
by 16%/12% in terms of translational/rotational errors on the KITTI dataset and
performs consistently well on the Apollo-Southbay datasets. We can even rival
the fully supervised counterparts with our mapping module and more unlabeled
training data.
