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：精确估计双目相机外参是保证立体匹配算法性能的关键。在先前的研究中，双目相机在线自标定通常被表述为一个专门的视觉里程计问题，而未考虑立体校正的原理。本文首先深入探讨了矫正单应矩阵的概念，它为我们提出的新型双目相机在线自标定算法（适用于仅有一对图像的情况）奠定了基石。此外，针对存在立体视频序列的情况，我们提出了一种简单而有效的全局最优外参估计方法。同时，我们指出使用三个欧拉角和三个平移向量分量进行性能量化在实际中不可行。为此，我们引入了四个新的评估指标，用于量化外参估计的鲁棒性和准确性，这些指标同时适用于单对和多对图像情况。通过室内外多种实验装置进行的大量实验验证了我们提出算法的有效性。综合评估结果表明，与基线算法相比，该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在mias.group/StereoCalibrator公开。