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.
Original language | English (US) |
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Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
DOIs | |
State | Published - 2023 |
Event | 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece Duration: Jun 4 2023 → Jun 10 2023 |
Keywords
- image interpolation
- LiDAR-camera calibration
- low-resolution point cloud
- supervised contrastive learning
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering