Extrinsic auto-calibration of LiDAR-camera system based on deep learning

Zifa Zhu; Yuebo Ma; Rujin Zhao; Enhai Liu

doi:10.1117/12.3025740

1 April 2024 Extrinsic auto-calibration of LiDAR-camera system based on deep learning

Zifa Zhu, Yuebo Ma, Rujin Zhao, Enhai Liu

Proceedings Volume 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023); 130811C (2024) https://doi.org/10.1117/12.3025740
Event: 2023 3rd International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 2023, Tianjin, China

Abstract

Precise geometrical extrinsic calibration is a prerequisite for fusing LiDAR and Camera information. Most existing calibration techniques necessitate a significant amount of calibration target data and human effort, resulting in a tedious and laborious procedure. Moreover, the relative poses between the LiDAR and the camera may drift cumulatively as the application time increases, and even the extrinsic parameters can change drastically following an accidental impact. This paper builds an extrinsic calibration end-to-end differentiable iterative refinement network. We combine the neural network with the geometric constraints of extrinsic calibration. Our model is trained entirely on the synthetic dataset and performs well when directly applied to the real-world dataset without fine-tuning, demonstrating our method's strong generalization capability.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zifa Zhu, Yuebo Ma, Rujin Zhao, and Enhai Liu "Extrinsic auto-calibration of LiDAR-camera system based on deep learning", Proc. SPIE 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 130811C (1 April 2024); https://doi.org/10.1117/12.3025740

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