Learning Moving-Object Tracking with FMCW LiDAR
This addresses moving-object tracking for autonomous driving systems, but it is incremental as it builds on existing tracking methods with a new sensor and learning approach.
The paper tackles moving-object tracking by using a new FMCW LiDAR sensor that provides Doppler velocity, enabling semi-automatic ground truth labeling and a contrastive learning framework to improve tracking, with results showing it outperforms baseline methods by a large margin.
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.