Learning-Based Framework for Camera Calibration with Distortion
Correction and High Precision Feature Detection
- URL: http://arxiv.org/abs/2202.00158v3
- Date: Sat, 29 Apr 2023 08:21:27 GMT
- Title: Learning-Based Framework for Camera Calibration with Distortion
Correction and High Precision Feature Detection
- Authors: Yesheng Zhang, Xu Zhao and Dahong Qian
- Abstract summary: We propose a hybrid camera calibration framework which combines learning-based approaches with traditional methods to handle these bottlenecks.
In particular, this framework leverages learning-based approaches to perform efficient distortion correction and robust chessboard corner coordinate encoding.
Compared with two widely-used camera calibration toolboxes, experiment results on both real and synthetic datasets manifest the better robustness and higher precision of the proposed framework.
- Score: 14.297068346634351
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Camera calibration is a crucial technique which significantly influences the
performance of many robotic systems. Robustness and high precision have always
been the pursuit of diverse calibration methods. State-of-the-art calibration
techniques based on classical Zhang's method, however, still suffer from
environmental noise, radial lens distortion and sub-optimal parameter
estimation. Therefore, in this paper, we propose a hybrid camera calibration
framework which combines learning-based approaches with traditional methods to
handle these bottlenecks. In particular, this framework leverages
learning-based approaches to perform efficient distortion correction and robust
chessboard corner coordinate encoding. For sub-pixel accuracy of corner
detection, a specially-designed coordinate decoding algorithm with embed
outlier rejection mechanism is proposed. To avoid sub-optimal estimation
results, we improve the traditional parameter estimation by RANSAC algorithm
and achieve stable results. Compared with two widely-used camera calibration
toolboxes, experiment results on both real and synthetic datasets manifest the
better robustness and higher precision of the proposed framework. The massive
synthetic dataset is the basis of our framework's decent performance and will
be publicly available along with the code at
https://github.com/Easonyesheng/CCS.
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