With the development of autonomous driving technology, sensor calibration has become a key technology to achieve accurate perception fusion and localization. Accurate calibration of the sensors ensures that each sensor can function properly and accurate information aggregation can be achieved. Among them, camera calibration based on surround view has received extensive attention. In autonomous driving applications, the calibration accuracy of the camera can directly affect the accuracy of perception and depth estimation. For online calibration of surround-view cameras, traditional feature extraction-based methods will suffer from strong distortion when the initial extrinsic parameters error is large, making these methods less robust and inaccurate. More existing methods use the sparse direct method to calibrate multi-cameras, which can ensure both accuracy and real-time performance and is theoretically achievable. However, this method requires a better initial value, and the initial estimate with a large error is often stuck in a local optimum. To this end, we introduce a robust automatic multi-cameras (pinhole or fisheye cameras) calibration and refinement method in the road scene. We utilize the coarse-to-fine random-search strategy, and it can solve large disturbances of initial extrinsic parameters, which can make up for falling into optimal local value in nonlinear optimization methods. In the end, quantitative and qualitative experiments are conducted in actual and simulated environments, and the result shows the proposed method can achieve accuracy and robustness performance. The open-source code is available at https://github.com/OpenCalib/SurroundCameraCalib.

翻译：随着自动驾驶技术的发展，传感器标定已成为实现精确感知融合与定位的关键技术。精确的传感器标定确保各传感器能正常运行，并实现准确的信息聚合。其中，基于环视的相机标定技术受到广泛关注。在自动驾驶应用中，相机的标定精度直接影响感知与深度估计的准确性。针对环视相机的在线标定，传统基于特征提取的方法在初始外参误差较大时会存在严重畸变，导致这些方法鲁棒性不足且精度降低。现有方法多采用稀疏直接法进行多相机标定，该方法既能保证精度与实时性，理论上也可行。然而，该方法需要较好的初始值，而误差较大的初始估计往往陷入局部最优。为此，我们提出一种鲁棒的自动多相机（针孔或鱼眼相机）标定与精化方法。该方法采用由粗到精的随机搜索策略，能够解决初始外参大扰动问题，弥补非线性优化方法易陷入局部最优的缺陷。最终在实际与仿真环境下进行了定量与定性实验，结果表明所提方法能达到精度与鲁棒性要求。开源代码见：https://github.com/OpenCalib/SurroundCameraCalib