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Enhanced Low-resolution LiDAR-Camera Calibration Via Depth Interpolation and Supervised Contrastive Learning

2022-11-08 01:12:08
Zhikang Zhang, Zifan Yu, Suya You, Raghuveer Rao, Sanjeev Agarwal, Fengbo Ren
arXiv_CV
         
Abstract

Motivated by the increasing application of low-resolution LiDAR recently, 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\textdegree on 32-channel LiDAR point cloud data, which significantly outperforms all reference methods.

Abstract (translated)

URL

https://arxiv.org/abs/2211.03932

PDF

https://arxiv.org/pdf/2211.03932.pdf

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