  An efficient 3D point cloud learning architecture, named EfficientLO-Net, for
LiDAR odometry is first proposed in this paper. In this architecture, the
projection-aware representation of the 3D point cloud is proposed to organize
the raw 3D point cloud into an ordered data form to achieve efficiency. The
Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task
is built to estimate and refine the pose in a coarse-to-fine approach. A
projection-aware attentive cost volume is built to directly associate two
discrete point clouds and obtain embedding motion patterns. Then, a trainable
embedding mask is proposed to weigh the local motion patterns to regress the
overall pose and filter outlier points. The trainable pose warp-refinement
module is iteratively used with embedding mask optimized hierarchically to make
the pose estimation more robust for outliers. The entire architecture is
holistically optimized end-to-end to achieve adaptive learning of cost volume
and mask, and all operations involving point cloud sampling and grouping are
accelerated by projection-aware 3D feature learning methods. The superior
performance and effectiveness of our LiDAR odometry architecture are
demonstrated on KITTI, M2DGR, and Argoverse datasets. Our method outperforms
all recent learning-based methods and even the geometry-based approach, LOAM
with mapping optimization, on most sequences of KITTI odometry dataset. We open
sourced our codes at: https://github.com/IRMVLab/EfficientLO-Net.
