The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Cloud-based robot grasping with the google object recognition engine
Abstract
Rapidly expanding internet resources and wireless networking have potential to liberate robots and automation systems from limited onboard computation, memory, and software. “Cloud Robotics” describes an approach that recognizes the wide availability of networking and incorporates open-source elements to greatly extend earlier concepts of “Online Robots” and “Networked Robots”. In this paper we consider how cloud-based data and computation can facilitate 3D robot grasping. We present a system architecture, implemented prototype, and initial experimental data for a cloud-based robot grasping system that incorporates a Willow Garage PR2 robot with onboard color and depth cameras, Google's proprietary object recognition engine, the Point Cloud Library (PCL) for pose estimation, Columbia University's GraspIt! toolkit and OpenRAVE for 3D grasping and our prior approach to sampling-based grasp analysis to address uncertainty in pose. We report data from experiments in recognition (a recall rate of 80% for the objects in our test set), pose estimation (failure rate under 14%), and grasping (failure rate under 23%) and initial results on recall and false positives in larger data sets using confidence measures.
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Cites
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Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Object Detection with Discriminatively Trained Part-Based Models
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Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
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“For each object oi, we also capture a set of reference images at different viewpoints φi,j for j ∈ [1, Noi ].”
BackgroundMetric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost
Mensink, T., Verbeek, J., Perronnin, F., & Csurka, G. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33709-3_35 · Cited by 281
Cited by
Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., & Quillen, D. (2018) · The International Journal of Robotics Research · doi:10.1177/0278364917710318 · Cited by 1,971
A Survey of Research on Cloud Robotics and Automation
Kehoe, B., Patil, S., Abbeel, P., & Goldberg, K. (2015) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2014.2376492 · Cited by 833
Self-citationJames, S., Wohlhart, P., Kalakrishnan, M., Kalashnikov, D., Irpan, A., Ibarz, J., Levine, S., Hadsell, R., & Bousmalis, K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01291 · Cited by 420
Mahler, J., Pokorny, F. T., Hou, B., Roderick, M., Laskey, M., Aubry, M., Kohlhoff, K., Kroger, T., Kuffner, J., & Goldberg, K. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487342 · Cited by 359
Self-citationGlimpse: Continuous, Real-Time Object Recognition on Mobile Devices
Chen, T. Y.-H., Ravindranath, L., Deng, S., Bahl, P., & Balakrishnan, H. (2015) · Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/2809695.2809711 · Cited by 358
Metadata
- Authors
- Kehoe, B., Matsukawa, A., Candido, S., Kuffner, J., & Goldberg, K.
- Venue
- 2013 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 4263-4270
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1986533639
- Cited by
- 228
- References
- 4
- FWCI
- 30.16
- Type
- conference paper
- Language
- en
- Open access
- closed
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