视觉系统的场景标定问题是乒乓球机器人研究中要解决的首要任务。针对传统的双目视觉方案中图像处理运算量大、成本高、高速同步采集实现较为困难的缺点，探索了一种单目摄像机下的基于影子辅助成像的视觉标定方法。从单目摄像机模型出发，简化立体映射为平面映射，利用视觉场景中灯光、乒乓球和影子之间的几何关系，结合最小二乘法和最小误差估计，准确地计算视觉系统中灯与摄像机的位置，为图像识别和3维目标定位奠定基础。该方法既标定了摄像机的参数，同时也标定了辅助灯的参数，而且从平面图像坐标计算3维坐标过程简便，实验结果证明其具有较高的精度（<4 mm），可应用于快速图像处理的场合（>60 fps），有望成为一种低成本、便捷而有效的方法。

Vision system calibration is the first and foremost task in ping-pong robot. To reduce the deficiency of the traditional binocular vision cameras method in heavy computation and difficulty in image synchronization, a new calibration algorithm is proposed to deal with a monocular vision system. Based on the monocular camera model，the stereo mapping is transferred to the planar mapping. Using the projection geometry relationship between light, ball and shadow, both the least square method and the optimized error estimation method are used to evaluate the parameters of the vision system. Furthermore, it lays a foundation of 3D position calculation and trace prediction. This method has been proved to have high accuracy to locate the object in the scene (<4 mm) and high-speed image processing (>60 fps). It is an effective and reliable way in computer vision compared with binocular stereo vision in structure size, measurement speed, and cost. Also it has a wide application prospect.