  A novel 3D point cloud learning model for deep LiDAR odometry, named
PWCLO-Net, using hierarchical embedding mask optimization is proposed in this
paper. In this model, the Pyramid, Warping, and Cost volume (PWC) structure for
the LiDAR odometry task is built to refine the estimated pose in a
coarse-to-fine approach hierarchically. An attentive cost volume is built to
associate two point clouds and obtain embedding motion patterns. Then, a novel
trainable embedding mask is proposed to weigh the local motion patterns of all
points to regress the overall pose and filter outlier points. The estimated
current pose is used to warp the first point cloud to bridge the distance to
the second point cloud, and then the cost volume of the residual motion is
built. At the same time, the embedding mask is optimized hierarchically from
coarse to fine to obtain more accurate filtering information for pose
refinement. The trainable pose warp-refinement process is iteratively used to
make the pose estimation more robust for outliers. The superior performance and
effectiveness of our LiDAR odometry model are demonstrated on KITTI odometry
dataset. Our method outperforms all recent learning-based methods and
outperforms the geometry-based approach, LOAM with mapping optimization, on
most sequences of KITTI odometry dataset.Our source codes will be released on
https://github.com/IRMVLab/PWCLONet.
