The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Persistent Navigation and Mapping using a Biologically Inspired SLAM System
Abstract
The challenge of persistent navigation and mapping is to develop an autonomous robot system that can simultaneously localize, map and navigate over the lifetime of the robot with little or no human intervention. Most solutions to the simultaneous localization and mapping (SLAM) problem aim to produce highly accurate maps of areas that are assumed to be static. In contrast, solutions for persistent navigation and mapping must produce reliable goal-directed navigation outcomes in an environment that is assumed to be in constant flux. We investigate the persistent navigation and mapping problem in the context of an autonomous robot that performs mock deliveries in a working office environment over a two-week period. The solution was based on the biologically inspired visual SLAM system, RatSLAM. RatSLAM performed SLAM continuously while interacting with global and local navigation systems, and a task selection module that selected between exploration, delivery, and recharging modes. The robot performed 1,143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), traveled a total distance of more than 40 km over 37 hours of active operation, and recharged autonomously a total of 23 times.
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Cites
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Cited by
Visual Place Recognition: A Survey
Lowry, S., Sünderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., & Milford, M. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2496823 · Cited by 1,079
Self-citationSeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights
Milford, M. J., & Wyeth, G. F. (2012) · 2012 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2012.6224623 · Cited by 981
Self-citationVisual teach and repeat for long‐range rover autonomy
Furgale, P., & Barfoot, T. D. (2010) · Journal of Field Robotics · doi:10.1002/rob.20342 · Cited by 266
Experience-based navigation for long-term localisation
Churchill, W., & Newman, P. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913499193 · Cited by 225
Tsintotas, K. A., Bampis, L., & Gasteratos, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3175656 · Cited by 175
“To limit the number of database instances, clustering [137], [247], [254], [358]–[360] or pruning [361] methods can be”
Method
Metadata
- Authors
- Milford, M., & Wyeth, G.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2009-07-21
- Volume
- 29
- Issue
- 9
- Pages
- 1131-1153
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2112164532
- Cited by
- 286
- References
- 46
- FWCI
- 29.06
- Type
- journal article
- Language
- en
- Open access
- closed
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