针对现有棋盘格角点自动提取算法鲁棒性不足的缺点，提出了使用直线检测算法（LSD）来进行角点的自动提取。首先，使用LSD算法对标定图像进行处理，得出包含棋盘格子边缘的所有直线，并分别对所得直线的长度和角度进行伪排序以去除伪格子边缘。然后，对剩余直线的边缘端点进行近邻合并，得到角点的初始坐标并进行亚像素优化。最后，使用能量法对角点进行棋盘格结构复原与排序。实验结果显示：该方法可以正确提取含有阴影和噪声的玻璃材质标定板图像中的角点。与改进的Harris方法的角点坐标提取精度对比试验得到其最大偏差小于0.2 pixel，平均偏差小于0.15 pixel， 表明该方法具有较高的鲁棒性且定位精度与改进的Harris方法相当，可用于工程实际中环境光源变化较大的场合。

To improve the robustness of current automatic corner detection algorithms, a novel algorithm based on Line Segment Detection( LSD ) was proposed to extract the corners automatically. First, the LSD algorithm was used to process a checkerboard image to obtain all lines including checker edges. Then, the pseudo permutation of lengths and angles for obtained lines were done respectively to filter fake edges. Furthermore, the neighboring endpoints of the remaining lines were combined , and the coordinates of the corners were optimized with the sub-pixel algorithm. Finally, an energy method was utilized to recover the chessboard's structure and the corner points were ranked at the same time. Experimental results indicate that method proposed here automatically detects corners in images with noises and shadows. The maximum locating error and average error for the corner coordinate extraction are less than 0.2 pixels and 0.15 pixels respectively as compared with those of modified Harris method. This method has a higher robustness and its locating accuracy is almost as the modified Harris method, which shows it is suitable for a real factory environment.