Next best measurement posture determination based on depth image and density clustering

被引：0
作者：
Lin J. [1 ,2 ]
Zhang J. [1 ,2 ]
Li L. [1 ,2 ]
Xiao Q. [1 ,2 ]
Jiang K. [1 ,2 ]
机构：
[1] Xiamen Key Laboratory of Digital Vision Measurement, Huaqiao University, Xiamen
[2] Fujian Provincial Key Laboratory of Special Energy Manufacturing, Huaqiao University, Xiamen
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2021年 / 27卷 / 11期
关键词：
Automatic measurement; Density clustering; Depth image; Next best measurement posture; Robot vision;
D O I：
10.13196/j.cims.2021.11.008
中图分类号：
学科分类号：
摘要：
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. © 2021, Editorial Department of CIMS. All right reserved.
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页码：3138 / 3147
页数：9
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