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 mias.group/StereoCalibrator.

翻译：双目相机外参的精确估计是保证立体匹配算法性能的关键。在现有技术中，双目相机的在线自标定通常被建模为一个专用的视觉里程计问题，而未考虑立体校正的原理。本文首先深入探讨了校正单应性矩阵的概念，以此为基石，针对仅有一对图像可用的情况，提出了一种新颖的双目相机在线自标定算法。进一步地，针对存在立体视频序列的情况，我们引入了一种简单高效的全局最优外参估计方法。此外，我们指出了使用三个欧拉角和三个平移向量分量进行性能量化评估的不可行性，并引入了四个新的评估指标来量化单图像对及多图像对情况下外参估计的鲁棒性与精度。通过在不同实验环境中采用多种实验设置进行的广泛实验，验证了所提算法的有效性。全面的评估结果表明，与基线算法相比，所提算法具有更优越的性能。我们的源代码、演示视频及补充材料已公开于mias.group/StereoCalibrator。