CalibDepth: Unifying Depth Map Representation for Iterative LiDAR-Camera Online Calibration
Jiangtong Zhu, Jianru Xue, Pu Zhang
Abstract
LiDAR-Camera online calibration is of great significance for building a stable autonomous driving perception system. For online calibration, a key challenge lies in constructing a unified and robust representation between multi-modal sensor data. Most methods extract features manually or implicitly with an end-to-end deep learning method. The former suffers poor robustness, while the latter has poor interpretability. In this paper, we propose CalibDepth, which uses depth maps as the unified representation for image and LiDAR point cloud. CalibDepth introduces a sub-network for monocular depth estimation to assist online calibration tasks. To further improve the performance, we regard online calibration as a sequence prediction problem, and introduce global and local losses to optimize the calibration results. CalibDepth shows excellent performance in different experimental setups. Code is open-sourced at https://github.com/Brickzhuantou/CalibDepth.
BibTeX
@inproceedings{icra2023_calibdepthunifyi,
title = {CalibDepth: Unifying Depth Map Representation for Iterative LiDAR-Camera Online Calibration},
author = {Jiangtong Zhu and Jianru Xue and Pu Zhang},
booktitle = {ICRA 2023},
year = {2023}
}