深入探究用于立体相机在线自标定的校正单应性
Dive Deeper into Rectifying Homography for Stereo Camera Online Self-Calibration
AI总结：
本文基于校正单应性提出立体相机在线自标定算法，支持单对图像与视频序列全局最优外参估计，并设计四项新指标，实验证明其优于基线。
摘要：
准确估计立体相机外参是保证立体匹配算法性能的关键。在以往研究中，立体相机的在线自标定通常被表述为一个专门的视觉里程计问题，而没有考虑立体校正的原理。本文首先深入研究校正单应性的概念，它是我们针对仅有一对图像可用情形所开发的新型立体相机在线自标定算法的基石。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解决方案。另外，我们强调使用三个欧拉角和平移向量中的三个分量来进行性能量化是不切实际的。为此，我们引入四项新的评价指标，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形。我们在室内和室外环境中使用多种实验装置开展了大量实验，验证了所提算法的有效性。综合评价结果表明，与基线算法相比，该算法具有更优越的性能。我们的源代码、演示视频和补充材料已公开发布于 mias.group/StereoCalibrator。
英文摘要：
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.
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