TR2012-007
Fast Object Localization and Pose Estimation in Heavy Clutter for Robotic Bin Picking
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- "Fast Object Localization and Pose Estimation in Heavy Clutter for Robotic Bin Picking", International Journal of Robotics Research, May 2012.BibTeX TR2012-007 PDF
- @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 = {https://www.merl.com/publications/TR2012-007}
- }
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- "Fast Object Localization and Pose Estimation in Heavy Clutter for Robotic Bin Picking", International Journal of Robotics Research, May 2012.
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MERL Contact:
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Research Areas:
Abstract:
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.
Related News & Events
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NEWS MERL researcher, Oncel Tuzel, gives keynote talk at 2016 International Symposium on Visual Computing Date: December 14, 2015 - December 16, 2015
Where: Las Vegas, NV, USA
Research Area: Machine LearningBrief- MERL researcher, Oncel Tuzel, gave a keynote talk at 2016 International Symposium on Visual Computing in Las Vegas, Dec. 16, 2015. The talk was titled: "Machine vision for robotic bin-picking: Sensors and algorithms" and reviewed MERL's research in the application of 2D and 3D sensing and machine learning to the problem of general pose estimation.
The talk abstract was: For over four years, at MERL, we have worked on the robot "bin-picking" problem: using a 2D or 3D camera to look into a bin of parts and determine the pose, 3D rotation and translation, of a good candidate to pick up. We have solved the problem several different ways with several different sensors. I will briefly describe the sensors and the algorithms. In the first half of the talk, I will describe the Multi-Flash camera, a 2D camera with 8 flashes, and explain how this inexpensive camera design is used to extract robust geometric features, depth edges and specular edges, from the parts in a cluttered bin. I will present two pose estimation algorithms, (1) Fast directional chamfer matching--a sub-linear time line matching algorithm and (2) specular line reconstruction, for fast and robust pose estimation of parts with different surface characteristics. In the second half of the talk, I will present a voting-based pose estimation algorithm applicable to 3D sensors. We represent three-dimensional objects using a set of oriented point pair features: surface points with normals and boundary points with directions. I will describe a max-margin learning framework to identify discriminative features on the surface of the objects. The algorithm selects and ranks features according to their importance for the specified task which leads to improved accuracy and reduced computational cost.
- MERL researcher, Oncel Tuzel, gave a keynote talk at 2016 International Symposium on Visual Computing in Las Vegas, Dec. 16, 2015. The talk was titled: "Machine vision for robotic bin-picking: Sensors and algorithms" and reviewed MERL's research in the application of 2D and 3D sensing and machine learning to the problem of general pose estimation.
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NEWS The International Journal of Robotics Research: publication by Yuichi Taguchi, Tim K. Marks, C. Oncel Tuzel, Ming-Yu Liu and others Date: May 8, 2012
Where: The International Journal of Robotics Research
MERL Contact: Tim K. Marks
Research Area: Computer VisionBrief- The article "Fast Object Localization and Pose Estimation in Heavy Clutter for Robotic Bin Picking" by Liu, M.-Y., Tuzel, O., Veeraraghavan, A., Taguchi, Y., Marks, T.K. and Chellappa, R. was published in The International Journal of Robotics Research.