针对机器人自适应打磨曲面焊接区域的识别问题，提出了一种基于线结构光的打磨机器人自动识别起始点与终止点的算法和一种深度图像增强算子。增强算子将中心像素与8邻域内像素强度差的绝对值和作为中心像素值，以增强深度图像的可视化特征和打磨区域的纹理特征。首先，对点云数据进行滤波和空洞填充处理；然后，计算每一扫描行点云在高度方向的标准差；最后，对所得特征进行识别，找到一定范围内特征变化较大的位置，从而提取出打磨区域。实验结果表明，该增强算子对深度图像的增强效果极佳，本算法对起始与终止位置的识别精度均值小于1 mm，可达到像素级精度，具有较强的鲁棒性，且对噪声不敏感。在现场打磨测试中也证实了本算法的有效性和可行性。

Aiming at the recognition problem of robot adaptive grinding curved surface welding area， an algorithm based on linear structured light for automatic recognition of starting and ending points of grinding robot and a depth image enhancement operator are proposed in this work. The enhancement operator takes the sum of the absolute value of the intensity difference between the center pixel and the pixels in the 8 neighboring regions as the value of the center pixel to enhance the visualization features of the depth image and reveal the texture features of the polished area. First， the point cloud data is filtered and the hole filling is processed； second， the standard deviation of each scan line point cloud in the height direction is calculated； finally， the obtained features are identified to find the position with large feature change in a certain range， so as to extract the area that needs grinding. Experimental results show that the enhancement operator has excellent effect on depth image enhancement， and the average recognition accuracy of the algorithm for the start and end positions is less than 1 mm， which can achieve pixel level accuracy， and has strong robustness and is insensitive to noise. The effectiveness and feasibility of the algorithm are also verified in the field test.