Contextual object category recognition for RGB-D scene labeling
Robotics and Autonomous Systems
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Humanoid robots such as DLR's Justin are built with light-weight structures and flexible mechanical components. These generate positioning errors at the TCP (Tool-Center-Point) end-pose of the hand. The identification of these errors is essential for object manipulation and path planning. We proposed a verification routine to identify the bounds of the TCP end-pose errors by using the on-board stereo vision system. It involves estimating the pose of 3D point clouds of Justin's hand by using state-of-the-art 3D registration techniques. Partial models of the hand were generated by registering subsets of overlapping 3D point clouds. We proposed a method for the selection of overlapping point clouds of self-occluding objects (Justin's hand). It is based on a statistical analysis of the depth values. We applied an extended metaview registration method to the resulting subset of point clouds. The partial models were evaluated with detailed based surface consistency measures. The TCP end-pose errors estimated by using our method are consistent with ground-truth errors.