  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.
