GreenPCO: An Unsupervised Lightweight Point Cloud Odometry Method
This work addresses motion tracking for autonomous vehicles or robotics using LiDAR data, offering a more efficient solution compared to deep learning methods, though it appears incremental as it builds on existing techniques like PointHop++.
The paper tackles the point cloud odometry problem by proposing GreenPCO, an unsupervised lightweight method that uses geometry-aware sampling and PointHop++ for feature extraction to estimate object motion from LiDAR scans. Experiments on the KITTI dataset show it outperforms deep learning benchmarks in accuracy with a smaller model size and less training time.
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.