The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Object recognition and full pose registration from a single image for robotic manipulation
Open accessAbstract
Robust perception is a vital capability for robotic manipulation in unstructured scenes. In this context, full pose estimation of relevant objects in a scene is a critical step towards the introduction of robots into household environments. In this paper, we present an approach for building metric 3D models of objects using local descriptors from several images. Each model is optimized to fit a set of calibrated training images, thus obtaining the best possible alignment between the 3D model and the real object. Given a new test image, we match the local descriptors to our stored models online, using a novel combination of the RANSAC and Mean Shift algorithms to register multiple instances of each object. A robust initialization step allows for arbitrary rotation, translation and scaling of objects in the test images. The resulting system provides markerless 6-DOF pose estimation for complex objects in cluttered scenes. We provide experimental results demonstrating orientation and translation accuracy, as well a physical implementation of the pose output being used by an autonomous robot to perform grasping in highly cluttered scenes.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Object recognition and pose estimation using color cooccurrence histograms and geometric modeling
Ekvall, S., Kragic, D., & Hoffmann, F. (2005) · Image and Vision Computing · doi:10.1016/j.imavis.2005.05.006 · Cited by 52
Cited by
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202133 · Cited by 3,124
“There has also been some success with only monocular camera images [5].”
BackgroundMethodData-Driven Grasp Synthesis—A Survey
Bohg, J., Morales, A., Asfour, T., & Kragic, D. (2014) · IEEE Transactions on Robotics · doi:10.1109/tro.2013.2289018 · Cited by 929
Deep visual foresight for planning robot motion
Finn, C., & Levine, S. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989324 · Cited by 599
Task Space Regions: A framework for pose-constrained manipulation planning
Berenson, D., Srinivasa, S., & Kuffner, J. (2011) · The International Journal of Robotics Research · doi:10.1177/0278364910396389 · Cited by 368
Self-citationUnsupervised feature learning for 3D scene labeling
Lai, K., Bo, L., & Fox, D. (2014) · 2014 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2014.6907298 · Cited by 317
Metadata
- Authors
- Collet, A., Berenson, D., Srinivasa, S. S., & Ferguson, D.
- Venue
- 2009 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2009-05
- Pages
- 48-55
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2146447720
- Cited by
- 275
- References
- 6
- FWCI
- 21.26
- Type
- conference paper
- Language
- en
- Open access
- green
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- Collet, A., Berenson, D., Srinivasa, S. S., & Ferguson, D. (2009). Object recognition and full pose registration from a single image for robotic manipulation. 2009 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/robot.2009.5152739
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