关键词: 机器人视觉, 自动测量, 深度图像, 密度聚类, 下一最佳测量位姿

Abstract: According to the problem of determining the next best measurement posture in automatic measurement of robot vision,a method of fusion depth image and density clustering was proposed.The depth image and 3D point clouds of the object under the initial view were obtained by using the structured light binocular vision measurement technology.Then the edge and density clustering area of the object could be gained quickly by depth image.The complexity of each edge region of the object was determined based on density clustering,and the weight of each sub-region was calculated in combination with the size of the field of view,so the best moving direction of the next field of view was estimated.Furthermore,trend surface analysis was adopted to predict the spatial range of the next best measurement position.Using the depth image information,the global area of the trend surface analysis and the spatial data of the central trend line could be quickly obtained to determine the next best measurement posture.The Universal Robot 5 (UR5) robot and the vision measurement platform was established to carry out the measurement experiment on the Huba and rabbit models,and the effectiveness of the proposed method was proved.

Key words: robot vision, automatic measurement, depth image, density clustering, next best measurement posture