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Accurate estimation of stereo camera extrinsic parameters is the key to guarantee the performance of stereo matching algorithms.
In prior arts, the online self-calibration of stereo cameras has commonly been formulated as a specialized visual odometry problem,
without taking into account the principles of stereo rectification. In this paper, we first delve deeply into the concept of rectifying
homography, which serves as the cornerstone for the development of our novel stereo camera online self-calibration algorithm, for cases
where only a single pair of images is available. Furthermore, we introduce a simple yet effective solution for global optimum extrinsic
parameter estimation in the presence of stereo video sequences. Additionally, we emphasize the impracticality of using three Euler angles
and three components in the translation vectors for performance quantification. Instead, we introduce four new evaluation metrics to
quantify the robustness and accuracy of extrinsic parameter estimation, applicable to both single-pair and multi-pair cases. Extensive
experiments conducted across indoor and outdoor environments using various experimental setups validate the effectiveness of our proposed
algorithm. The comprehensive evaluation results demonstrate its superior performance in comparison to the baseline algorithm. Our source
code, demo video, and supplement are publicly available at
at https://mias.group/StereoCalibrator/.
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