Enhanced Low-Resolution LiDAR-Camera Calibration via Depth Interpolation and Supervised Contrastive Learning

Zhikang Zhang; Zifan Yu; Suya You; Raghuveer Rao; Sanjeev Agarwal; Fengbo Ren

ICASSP 2023accepted0 citations

Enhanced Low-Resolution LiDAR-Camera Calibration via Depth Interpolation and Supervised Contrastive Learning

Zhikang Zhang, Zifan Yu, Suya You, Raghuveer Rao, Sanjeev Agarwal, Fengbo Ren

Abstract

Motivated by the increasing application of low-resolution LiDAR, we target the problem of low-resolution LiDAR-camera calibration in this work. The main challenges are two-fold: sparsity and noise in point clouds. To address the problem, we propose to apply depth interpolation to increase the point density and supervised contrastive learning to learn noise-resistant features. The experiments on RELLIS-3D demonstrate that our approach achieves an average mean absolute rotation/translation errors of 0.15cm/0.33° on 32-channel LiDAR point cloud data, which significantly outperforms all reference methods.

BibTeX

@inproceedings{icassp2023_enhancedlowresol,
  title = {Enhanced Low-Resolution LiDAR-Camera Calibration via Depth Interpolation and Supervised Contrastive Learning},
  author = {Zhikang Zhang and Zifan Yu and Suya You and Raghuveer Rao and Sanjeev Agarwal and Fengbo Ren},
  booktitle = {ICASSP 2023},
  year = {2023}
}