Lee Wonju
The School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea.
CTO Division, LG Electronics, Seoul 07796, Republic of Korea.
Sensors (Basel). 2025 Apr 18;25(8):2565. doi: 10.3390/s25082565.
Stereo cameras and stereo matching algorithms are core components for stereo digital image correlation to obtain 3D data robustly in various environments. However, its accuracy heavily relies on extrinsic calibration. In this work, we propose a markerless method for obtaining stereo extrinsic calibration by employing nonlinear optimization on a manifold, which leverages the inherent observability property. To ensure the stability of the optimization and the robustness to outliers when using natural features, we minimize the error constraint between spatial per-frame sparse natural features by stably combining cost functions with similar properties, considering cost uncertainty. Both constraints work in the same direction to reduce the difference in the y-axis coordinates of corresponding points. As a result, the optimization process proceeds smoothly, and it helps reduce the likelihood of overfitting. To extend the problem to the spatiotemporal domain, Bayesian filtering is applied using the logit of zero-shot-based semantic segmentation. Using publicly available data, we conducted experiments where the optimization converged with minimal variation in the number of iterations, and stability was validated through a comparison with state-of-the-art methods.
立体相机和立体匹配算法是立体数字图像相关技术的核心组件，用于在各种环境中稳健地获取三维数据。然而，其精度严重依赖于外部校准。在这项工作中，我们提出了一种无标记方法，通过在流形上进行非线性优化来获得立体外部校准，该方法利用了固有的可观测性特性。为了确保优化的稳定性以及在使用自然特征时对异常值的鲁棒性，我们通过稳定地组合具有相似特性的代价函数并考虑代价不确定性，来最小化空间每帧稀疏自然特征之间的误差约束。这两个约束朝着相同方向起作用，以减少对应点在y轴坐标上的差异。结果，优化过程顺利进行，并且有助于降低过拟合的可能性。为了将该问题扩展到时空域，使用基于零样本语义分割的对数几率应用贝叶斯滤波。利用公开可用的数据，我们进行了实验，优化在迭代次数变化最小的情况下收敛，并通过与现有方法的比较验证了稳定性。
