TR2012-007

Fast Object Localization and Pose Estimation in Heavy Clutter for Robotic Bin Picking


    •  Liu, M.-Y.; Tuzel, O.; Veeraraghavan, A.; Taguchi, Y.; Marks, T.K.; Chellappa, R., "Fast Object Localization and Pose Estimation in Heavy Clutter for Robotic Bin Picking", International Journal of Robotics Research, May 2012.
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      • @article{Liu2012may,
      • author = {Liu, M.-Y. and Tuzel, O. and Veeraraghavan, A. and Taguchi, Y. and Marks, T.K. and Chellappa, R.},
      • title = {Fast Object Localization and Pose Estimation in Heavy Clutter for Robotic Bin Picking},
      • journal = {International Journal of Robotics Research},
      • year = 2012,
      • month = may,
      • url = {http://www.merl.com/publications/TR2012-007}
      • }
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  • Research Area:

    Computer Vision


We present a practical vision-based robotic bin-picking system that performs detection and 3D pose estimation of objects in an unstructured bin using a novel camera design, picks up parts from the bin, and performs error detection and pose correction while the part is in the gripper. Two main innovations enable our system to achieve real-time robust and accurate operation. First, we use a multi-flash camera that extracts robust depth edges. Second, we introduce an efficient shape-matching algorithm called fast directional chamfer matching (FDCM), which is used to reliably detect objects and estimate their poses. FDCM improves the accuracy of chamfer matching by including edge orientation. It also achieves massive improvements in matching speed using line-segment approximations of edges, a 3D distance transform, and directional integral images. We empirically show that these speedups, combined with the use of bounds in the spatial and hypothesis domains, give the algorithm sublinear computational complexity. We also apply our FDCM method to other applications in the context of deformable and articulated shape matching. In addition to significantly improving upon the accuracy of previous chamfer matching methods in all of the evaluated applications, FDCM is up to two orders of magnitude faster than the previous methods.