Guan Banglei, Shang Yang, Yu Qifeng
Appl Opt. 2017 Nov 20;56(33):9257-9267. doi: 10.1364/AO.56.009257.
In this paper, we present a robust technique of stereo calibration using homography constraints. Our method is novel as stereo calibration is performed by solving a polynomial equation system including two radial distortion parameters, using a minimal number of five image point correspondences. This enables us to calibrate from only a pair of stereo images of a planar scene, and to provide the exact algebraic solution to the stereo calibration problem. The minimal case solution is useful to reduce the computation time and increase the calibration robustness when using random sample consensus (RANSAC) from the correspondences of the stereo image pair. Further, a non-linear parameter optimization for the intrinsic and extrinsic parameters of stereo cameras is performed using the inliers, which are determined after RANSAC. In addition, our method can achieve more robust calibration results with multiple stereo image pairs by performing joint optimization. In contrast to the previous stereo calibration methods, our method works without requiring any special hardware and has no problems with one stereo image pair, even corrupted by severe radial distortions. Finally, by evaluating our method on both synthetic and real scene data, we demonstrate that our method is both efficient and accurate for stereo calibration.
在本文中，我们提出了一种利用单应性约束进行立体校准的稳健技术。我们的方法具有创新性，因为立体校准是通过求解一个包含两个径向畸变参数的多项式方程组来完成的，仅使用最少的五个图像点对应关系。这使我们能够仅从平面场景的一对立体图像进行校准，并为立体校准问题提供精确的代数解。当从立体图像对的对应关系中使用随机抽样一致性（RANSAC）时，最小情况解有助于减少计算时间并提高校准的稳健性。此外，使用在RANSAC之后确定的内点对立体相机的内参和外参进行非线性参数优化。此外，我们的方法通过执行联合优化，使用多个立体图像对可以获得更稳健的校准结果。与先前的立体校准方法相比，我们的方法无需任何特殊硬件即可工作，并且对于一对立体图像，即使受到严重的径向畸变影响也没有问题。最后，通过在合成数据和真实场景数据上评估我们的方法，我们证明了我们的方法对于立体校准既高效又准确。
