Data Association for Semantic World Modeling from Partial Views
Author(s)Wong, Lok Sang Lawson; Kaelbling, Leslie P.; Lozano-Perez, Tomas
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
International Journal of Robotics Research
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.
Author's final manuscript