GreenPCO: An Unsupervised Lightweight Point Cloud Odometry Method

TL;DR

GreenPCO is an unsupervised, lightweight point cloud odometry method that accurately estimates object motion using only LiDAR scans, outperforming deep learning models in accuracy with less training and smaller size.

Contribution

It introduces GreenPCO, a novel unsupervised approach for point cloud odometry that combines geometry-aware sampling and feature extraction for efficient motion estimation.

Findings

GreenPCO outperforms deep learning methods in accuracy.

GreenPCO has a significantly smaller model size.

GreenPCO requires less training time.

Abstract

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.