Simultaneous local and global state estimation for robotic navigation
Name
Moore-2009-Simultaneous local and global state estimation for robotic navigation.pdf
Size
1.5 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Moore, David C.
Huang, Albert S.
Walter, Matthew R.
Olson, Edwin B.
Fletcher, Luke Sebastian
Leonard, John Joseph
Teller, Seth
Date Issued
July 2009
Journal
Proceedings of the IEEE International Conference on Robotics and Automation, 2009
Publisher
Institute of Electrical and Electronics Engineers
Citation
Moore, D.C. et al. “Simultaneous local and global state estimation for robotic navigation.” Robotics and Automation, 2009. ICRA '09. IEEE International Conference on. 2009. 3794-3799. © Copyright 2010 IEEE
Version
Final published version
Abstract
Recent applications of robotics often demand two types of spatial awareness: 1) A fine-grained description of the robot's immediate surroundings for obstacle avoidance and planning, and 2) Knowledge of the robot's position in a large-scale global coordinate frame such as that provided by GPS. Although managing information at both of these scales is often essential to the robot's purpose, each scale has different requirements in terms of state representation and handling of uncertainty. In such a scenario, it can be tempting to pick either a body-centric coordinate frame or a globally fixed coordinate frame for all state representation. Although both choices have advantages, we show that neither is ideal for a system that must handle both global and local data. This paper describes an alternative design: a third coordinate frame that stays fixed to the local environment over short time-scales, but can vary with respect to the global frame. Careful management of uncertainty in this local coordinate frame makes it well-suited for simultaneously representing both locally and globally derived data, greatly simplifying system design and improving robustness. We describe the implementation of this coordinate frame and its properties when measuring uncertainty, and show the results of applying this approach to our 2007 DARPA Urban Challenge vehicle.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ROBOT.2009.5152763
