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：立体相机外参的精确估计是保障立体匹配算法性能的关键。现有研究中，立体相机在线自标定通常被形式化为专门的视觉里程计问题，却未考虑立体矫正的原理。本文首先深入剖析矫正单应矩阵的概念——该矩阵构成了我们提出的新型立体相机在线自标定算法（适用于仅有单对图像场景）的理论基石。进一步地，针对立体视频序列，我们提出一种简洁而高效的全局最优外参估计方案。此外，我们强调使用三个欧拉角及平移向量三分量进行性能定量评估的不实用性，转而引入四个全新的评价指标用以量化外参估计的鲁棒性与精度，这些指标同时适用于单对和多对图像场景。通过室内外多种实验配置下的大量验证，本文算法有效性得到充分证实。综合评估结果表明，与基线算法相比，本算法展现出显著优越性。相关源码、演示视频及补充材料已公开于 mias.group/StereoCalibrator。