Collision-free state estimation
Name
Lozano-Perez_Collision-free.pdf
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490.6 KB
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Checksum (MD5)
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Author(s)
Lozano-Perez, Tomas
Wong, Lok Sang Lawson
Kaelbling, Leslie P.
Date Issued
May 2012
Journal
Proceedings of the 2012 IEEE International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Wong, Lawson L.S., Leslie Pack Kaelbling, and Tomas Lozano-Perez. “Collision-Free State Estimation.” 2012 IEEE International Conference on Robotics and Automation (May 2012).
Version
Author's final manuscript
Abstract
In state estimation, we often want the maximum likelihood estimate of the current state. For the commonly used joint multivariate Gaussian distribution over the state space, this can be efficiently found using a Kalman filter. However, in complex environments the state space is often highly constrained. For example, for objects within a refrigerator, they cannot interpenetrate each other or the refrigerator walls. The multivariate Gaussian is unconstrained over the state space and cannot incorporate these constraints. In particular, the state estimate returned by the unconstrained distribution may itself be infeasible. Instead, we solve a related constrained optimization problem to find a good feasible state estimate. We illustrate this for estimating collision-free configurations for objects resting stably on a 2-D surface, and demonstrate its utility in a real robot perception domain.
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
https://doi.org/10.1109/ICRA.2012.6225309
