The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Markov Localization for Mobile Robots in Dynamic Environments
Open accessAbstract
Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This papers presents a version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments. The key idea of Markov localization is to maintain a probability density over the space of all locations of a robot in its environment. Our approach represents this space metrically, using a fine-grained grid to approximate densities. It is able to globally localize the robot from scratch and to recover from localization failures. It is robust to approximate models of the environment (such as occupancy grid maps) and noisy sensors (such as ultrasound sensors). Our approach also includes a filtering technique which allows a mobile robot to reliably estimate its position even in densely populated environments in which crowds of people block the robot's sensors for extended periods of time. The method described here has been implemented and tested in several real-world applications of mobile robots, including the deployments of two mobile robots as interactive museum tour-guides.
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Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
Globally Consistent Range Scan Alignment for Environment Mapping
Lu, F., & Milios, E. (1997) · Autonomous Robots · doi:10.1023/a:1008854305733 · Cited by 1,304
Mobile robot localization by tracking geometric beacons
Leonard, J. J., & Durrant-Whyte, H. F. (1991) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.88147 · Cited by 1,245
Cited by
Robust Monte Carlo localization for mobile robots
Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00069-8 · Cited by 1,809
Self-citationRobotics-based location sensing using wireless ethernet
Ladd, A. M., Bekris, K. E., Rudys, A., Marceau, G., Kavraki, L. E., & Wallach, D. S. (2002) · Proceedings of the 8th annual international conference on Mobile computing and networking · doi:10.1145/570645.570674 · Cited by 465
Model based vehicle detection and tracking for autonomous urban driving
Petrovskaya, A., & Thrun, S. (2009) · Autonomous Robots · doi:10.1007/s10514-009-9115-1 · Cited by 430
Self-citationDistributed Multirobot Exploration and Mapping
Fox, D., Ko, J., Konolige, K., Limketkai, B., Schulz, D., & Stewart, B. (2006) · Proceedings of the IEEE · doi:10.1109/jproc.2006.876927 · Cited by 344
Self-citationSliding window filter with application to planetary landing
Sibley, G., Matthies, L., & Sukhatme, G. (2010) · Journal of Field Robotics · doi:10.1002/rob.20360 · Cited by 259
Metadata
- Authors
- Fox, D., Burgard, W., & Thrun, S.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 1999-11-23
- Volume
- 11
- Pages
- 391-427
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W1601378499
- Cited by
- 904
- References
- 3
- FWCI
- 65.86
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Fox, D., Burgard, W., & Thrun, S. (1999). Markov Localization for Mobile Robots in Dynamic Environments. Journal of Artificial Intelligence Research. https://doi.org/10.1613/jair.616
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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