  Visual odometry aims to track the incremental motion of an object using the
information captured by visual sensors. In this work, we study the point cloud
odometry problem, where only the point cloud scans obtained by the LiDAR (Light
Detection And Ranging) are used to estimate object's motion trajectory. A
lightweight point cloud odometry solution is proposed and named the green point
cloud odometry (GreenPCO) method. GreenPCO is an unsupervised learning method
that predicts object motion by matching features of consecutive point cloud
scans. It consists of three steps. First, a geometry-aware point sampling
scheme is used to select discriminant points from the large point cloud.
Second, the view is partitioned into four regions surrounding the object, and
the PointHop++ method is used to extract point features. Third, point
correspondences are established to estimate object motion between two
consecutive scans. Experiments on the KITTI dataset are conducted to
demonstrate the effectiveness of the GreenPCO method. It is observed that
GreenPCO outperforms benchmarking deep learning methods in accuracy while it
has a significantly smaller model size and less training time.
