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Reference TypeConference Paper
Author(s)Bohg, J.;Romero, J.;Herzog, A.;Schaal, S.
TitleRobot Arm Pose Estimation through Pixel-Wise Part Classification
Journal/Conference/Book TitleProceedings of the IEEE International Conference on Robotics and Automation
KeywordsComputer vision, RGB-D perception, visual servoing
AbstractWe propose to frame the problem of marker-less robot arm pose estimation as a pixel-wise part classification problem. As input, we use a depth image in which each pixel is classified to be either from a particular robot part or the background. The classifier is a random decision forest trained on a large number of synthetically generated and labeled depth images. From all the training samples ending up at a leaf node, a set of offsets is learned that votes for relative joint positions. Pooling these votes over all foreground pixels and subsequent clustering gives us an estimate of the true joint positions. Due to the intrinsic parallelism of pixel-wise classification, this approach can run in super real-time and is more efficient than previous ICP-like methods. We quantitatively evaluate the accuracy of this approach on synthetic data. We also demonstrate that the method produces accurate joint estimates on real data despite being purely trained on synthetic data.
Place PublishedHongKong, PC
Short TitleRobot Arm Pose Estimation through Pixel-Wise Part Classification
Custom 3papers3://publication/uuid/B6F92602-EC23-4734-92F0-1D8A521C2133
Accession Number1165674204
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