Data Association for Semantic World Modeling from Partial Views
Name
Kaelbling_Data association.pdf
Size
6.25 MB
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Adobe PDF
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8aa99f9d00c51efd877c43e8f0a30052
Author(s)
Wong, Lok Sang Lawson
Kaelbling, Leslie P.
Lozano-Perez, Tomas
Date Issued
January 16, 2015
Journal
International Journal of Robotics Research
Publisher
Sage Publications
Citation
Wong, L.S. Lawson, Leslie Pack Kaelbling, and Tomas Lozano-Perez. "Data Association for Semantic World Modeling from Partial Views." International Journal of Robotics Research, June 2015; 34 (7) : 1064–1082.
Version
Author's final manuscript
Abstract
Autonomous mobile-manipulation robots need to sense and interact with objects to accomplish high-level tasks such as preparing meals and searching for objects. To achieve such tasks, robots need semantic world models, defined as object-based representations of the world involving task-level attributes. In this work, we address the problem of estimating world models from semantic perception modules that provide noisy observations of attributes. Because attribute detections are sparse, ambiguous, and are aggregated across different viewpoints, it is unclear which attribute measurements are produced by the same object, so data association issues are prevalent. We present novel clustering-based approaches to this problem, which are more efficient and require less severe approximations compared to existing tracking-based approaches. These approaches are applied to data containing object type-and-pose detections from multiple viewpoints, and demonstrate comparable quality using a fraction of the computation time.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1177/0278364914559754
https://doi.org/10.1177/0278364914559754
