CalibFormer: A Transformer-based Automatic LiDAR-Camera Calibration Network

• URL: http://arxiv.org/abs/2311.15241v2
• Date: Sun, 17 Mar 2024 05:30:40 GMT
• Title: CalibFormer: A Transformer-based Automatic LiDAR-Camera Calibration Network
• Authors: Yuxuan Xiao, Yao Li, Chengzhen Meng, Xingchen Li, Jianmin Ji, Yanyong Zhang,
• Abstract summary: CalibFormer is an end-to-end network for automatic LiDAR-camera calibration. We aggregate multiple layers of camera and LiDAR image features to achieve high-resolution representations. Our method achieved a mean translation error of $0.8751 mathrmcm$ and a mean rotation error of $0.0562 circ$ on the KITTI dataset.
• Score: 11.602943913324653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The fusion of LiDARs and cameras has been increasingly adopted in autonomous driving for perception tasks. The performance of such fusion-based algorithms largely depends on the accuracy of sensor calibration, which is challenging due to the difficulty of identifying common features across different data modalities. Previously, many calibration methods involved specific targets and/or manual intervention, which has proven to be cumbersome and costly. Learning-based online calibration methods have been proposed, but their performance is barely satisfactory in most cases. These methods usually suffer from issues such as sparse feature maps, unreliable cross-modality association, inaccurate calibration parameter regression, etc. In this paper, to address these issues, we propose CalibFormer, an end-to-end network for automatic LiDAR-camera calibration. We aggregate multiple layers of camera and LiDAR image features to achieve high-resolution representations. A multi-head correlation module is utilized to identify correlations between features more accurately. Lastly, we employ transformer architectures to estimate accurate calibration parameters from the correlation information. Our method achieved a mean translation error of $0.8751 \mathrm{cm}$ and a mean rotation error of $0.0562 ^{\circ}$ on the KITTI dataset, surpassing existing state-of-the-art methods and demonstrating strong robustness, accuracy, and generalization capabilities.

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