Accurate detection and localization of X-corner on both planar and non-planar patterns is a core step in robotics and machine vision. However, previous works could not make a good balance between accuracy and robustness, which are both crucial criteria to evaluate the detectors performance. To address this problem, in this paper we present a novel detection algorithm which can maintain high sub-pixel precision on inputs under multiple interference, such as lens distortion, extreme poses and noise. The whole algorithm, adopting a coarse-to-fine strategy, contains a X-corner detection network and three post-processing techniques to distinguish the correct corner candidates, as well as a mixed sub-pixel refinement technique and an improved region growth strategy to recover the checkerboard pattern partially visible or occluded automatically. Evaluations on real and synthetic images indicate that the presented algorithm has the higher detection rate, sub-pixel accuracy and robustness than other commonly used methods. Finally, experiments of camera calibration and pose estimation verify it can also get smaller re-projection error in quantitative comparisons to the state-of-the-art.

翻译：平面与非平面图案上X角点的精确检测与定位是机器人学与机器视觉的核心步骤。然而，现有方法难以兼顾精度与鲁棒性这两个评估检测器性能的关键指标。针对该问题，本文提出一种新型检测算法，可在镜头畸变、极端位姿及噪声等多重干扰下保持亚像素级高精度。该算法采用从粗到精的策略，包含X角点检测网络及三种后处理技术以区分正确角点候选，同时结合混合亚像素细化技术与改进的区域生长策略，实现部分遮挡或不可见棋盘格图案的自动恢复。在真实与合成图像上的评估表明，该算法相较于其他常用方法具有更高的检测率、亚像素精度及鲁棒性。最后，相机标定与位姿估计实验验证，其在与现有最先进方法的定量比较中可获得更小的重投影误差。