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A marker-less assembly stage recognition method based on segmented projection contour

机译:基于分段投影轮廓的较少标记组装阶段识别方法

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In man-machine cooperative assembly, assembly recognition that determines the current manual working stage is key information to driving automatic computer-aided assistance. Focused on three features of the assembly scene-movable view, process stage, and CAD model template- a view-free and marker-less assembly stage recognition method is proposed in this paper. By constructing the semantic model for the assembly scene and the stage model for CAD parts, a depth image of assembly and a CAD model can be extracted as point clouds. Then we propose the segmented projection contour descriptor to uniformly express the shape information as a series of contours, so the 3D registration issue is converted to a 2D registration issue. The vertex-to-edge Hausdorff distance is proposed in the partial registration to determine the transformation matrix for each pair of contours. Finally, the overall matching algorithm based on the overlay ratio is given, and the best matching stage model indicates the current assembly stage. The recognition and classification experiments are carried out to verify the proposed method. A comparison with traditional Hausdorff distance proves the proposed algorithm performs better in stage recognition. Our study reveals that the proposed view-free and marker-less method can solve the stage recognition issue based on the assembly's depth image, so as to connect the on-site assembly with the digital information.
机译:在人机协作组件中,确定当前手动工作阶段的装配识别是驾驶自动计算机辅助辅助的关键信息。专注于组装场景可移动视图,过程阶段和CAD模型模板的三个特征 - 在本文中提出了一种无视图和更小的装配级识别方法。通过构建组装场景的语义模型和CAD部件的舞台模型,可以将组装和CAD模型的深度图像作为点云提取。然后,我们提出分段投影轮廓描述符以将形状信息均匀地表达为一系列轮廓,因此3D注册问题被转换为2D注册问题。在部分注册中提出了顶点到边缘Hausdorff距离以确定每对轮廓的变换矩阵。最后,给出了基于覆盖率的整体匹配算法,并且最佳匹配阶段模型表示当前的组装级。进行识别和分类实验以验证提出的方法。与传统Hausdorff距离的比较证明了所提出的算法在阶段识别中表现更好。我们的研究表明,拟议的视图和较少的方法可以根据程序集的深度图像解决舞台识别问题,以便将现场装配与数字信息连接。

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