  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.

Computer Vision and Pattern Recognition