Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation
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Leonard_Two-stage focused.pdf
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2 MB
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Author(s)
Mu, Beipeng
Agha-mohammadi, Ali-akbar
Paull, Liam
Graham, Matthew
How, Jonathan P.
Leonard, John Joseph
Date Issued
July 2015
Journal
Proceedings of the 2015 Robotics: Science and Systems Conference
Citation
Mu, Beipeng, Ali-akbar Agha-mohammadi, Liam Paull, Matthew Graham, Jonathan How, John Leonard. "Two-Stage Focused Inference for Resource-Constrained Collision-Free Navigation." 2015 Robotics: Science and Systems Conference (July 2015).
Version
Author's final manuscript
Abstract
Long-term operations of resource-constrained robots typically require hard decisions be made about which data to process and/or retain. The question then arises of how to choose which data is most useful to keep to achieve the task at hand. As spacial scale grows, the size of the map will grow without bound, and as temporal scale grows, the number of measurements will grow without bound. In this work, we present the first known approach to tackle both of these issues. The approach has two stages. First, a subset of the variables (focused variables) is selected that are most useful for a particular task. Second, a task-agnostic and principled method (focused inference) is proposed to select a subset of the measurements that maximizes the information over the focused variables. The approach is then applied to the specific task of robot navigation in an obstacle-laden environment. A landmark selection method is proposed to minimize the probability of collision and then select the set of measurements that best localizes those landmarks. It is shown that the two-stage approach outperforms both only selecting measurement and only selecting landmarks in terms of minimizing the probability of collision. The performance improvement is validated through detailed simulation and real experiments on a Pioneer robot.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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DOI of Published Version
http://www.roboticsproceedings.org/rss11/p04.html
