Simultaneous local and global state estimation for robotic navigation
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 G...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/59992 https://orcid.org/0000-0002-8863-6550 |
| _version_ | 1811088577234206720 |
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| author | Moore, David C. Huang, Albert S. Walter, Matthew R. Olson, Edwin B. Fletcher, Luke Sebastian Leonard, John Joseph Teller, Seth |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Moore, David C. Huang, Albert S. Walter, Matthew R. Olson, Edwin B. Fletcher, Luke Sebastian Leonard, John Joseph Teller, Seth |
| author_sort | Moore, David C. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:04:14Z |
| format | Article |
| id | mit-1721.1/59992 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:04:14Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/599922022-09-28T18:09:30Z Simultaneous local and global state estimation for robotic navigation Moore, David C. Huang, Albert S. Walter, Matthew R. Olson, Edwin B. Fletcher, Luke Sebastian Leonard, John Joseph Teller, Seth 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 Teller, Seth Moore, David C. Huang, Albert S. Walter, Matthew R. Fletcher, Luke Sebastian Leonard, John Joseph Teller, Seth 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. United States. Defense Advanced Research Projects Agency (DARPA) (Urban Challenge, ARPA Order No. W369/00, Program Code: DIRO) (Contract No. HR0011-06-C-0149) 2010-11-15T15:38:12Z 2010-11-15T15:38:12Z 2009-07 2009-05 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-2788-8 1050-4729 INSPEC Accession Number: 10749010 http://hdl.handle.net/1721.1/59992 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 https://orcid.org/0000-0002-8863-6550 en_US http://dx.doi.org/10.1109/ROBOT.2009.5152763 Proceedings of the IEEE International Conference on Robotics and Automation, 2009 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. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Moore, David C. Huang, Albert S. Walter, Matthew R. Olson, Edwin B. Fletcher, Luke Sebastian Leonard, John Joseph Teller, Seth Simultaneous local and global state estimation for robotic navigation |
| title | Simultaneous local and global state estimation for robotic navigation |
| title_full | Simultaneous local and global state estimation for robotic navigation |
| title_fullStr | Simultaneous local and global state estimation for robotic navigation |
| title_full_unstemmed | Simultaneous local and global state estimation for robotic navigation |
| title_short | Simultaneous local and global state estimation for robotic navigation |
| title_sort | simultaneous local and global state estimation for robotic navigation |
| url | http://hdl.handle.net/1721.1/59992 https://orcid.org/0000-0002-8863-6550 |
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