Learning Moving-Object Tracking with FMCW LiDAR

TL;DR

This paper introduces a learning-based moving-object tracking method using FMCW LiDAR, which provides Doppler velocity information to enhance tracking accuracy, validated by extensive experiments on real driving data.

Contribution

The paper presents a novel FMCW LiDAR-based tracking approach with a contrastive learning framework and semi-automatic labeling, improving tracking performance over existing methods.

Findings

Outperforms baseline methods significantly

Utilizes Doppler velocity for better feature discrimination

Effective on real-world driving data

Abstract

In this paper, we propose a learning-based moving-object tracking method utilizing our newly developed LiDAR sensor, Frequency Modulated Continuous Wave (FMCW) LiDAR. Compared with most existing commercial LiDAR sensors, our FMCW LiDAR can provide additional Doppler velocity information to each 3D point of the point clouds. Benefiting from this, we can generate instance labels as ground truth in a semi-automatic manner. Given the labels, we propose a contrastive learning framework, which pulls together the features from the same instance in embedding space and pushes apart the features from different instances, to improve the tracking quality. Extensive experiments are conducted on our recorded driving data, and the results show that our method outperforms the baseline methods by a large margin.