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 globally 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：立体相机外参的精确估计是保证立体匹配算法性能的关键。现有技术中，立体相机的在线自标定通常被表述为一种特殊的视觉里程计问题，而未充分考虑立体矫正的原理。本文中，我们首先深入探究矫正单应矩阵的概念，并将其作为开发新型立体相机在线自标定算法的基石，适用于仅有一对图像可用的情况。此外，我们针对立体视频序列引入了一种简单而有效的全局最优外参估计方案。同时，我们强调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。取而代之，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精度，同样适用于单对和多对图像情况。在室内外环境中使用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公开于 mias.group/StereoCalibrator。