The DOI matches its Crossref record; 22 of the 23 references it cites were found in a registry or an index.
Experience-based navigation for long-term localisation
Abstract
This paper is about long-term navigation in environments whose appearance changes over time, suddenly or gradually. We describe, implement and validate an approach which allows us to incrementally learn a model whose complexity varies naturally in accordance with variation of scene appearance. It allows us to leverage the state of the art in pose estimation to build over many runs, a world model of sufficient richness to allow simple localisation despite a large variation in conditions. As our robot repeatedly traverses its workspace, it accumulates distinct visual experiences that in concert, implicitly represent the scene variation: each experience captures a visual mode. When operating in a previously visited area, we continually try to localise in these previous experiences while simultaneously running an independent vision-based pose estimation system. Failure to localise in a sufficient number of prior experiences indicates an insufficient model of the workspace and instigates the laying down of the live image sequence as a new distinct experience. In this way, over time we can capture the typical time-varying appearance of an environment and the number of experiences required tends to a constant. Although we focus on vision as a primary sensor throughout, the ideas we present here are equally applicable to other sensor modalities. We demonstrate our approach working on a road vehicle operating over a 3-month period at different times of day, in different weather and lighting conditions. We present extensive results analysing different aspects of the system and approach, in total processing over 136,000 frames captured from 37 km of driving.
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“This has led to develop new methods that explicitly account for such variations by matching sequences [173], gathering different visual appearances into a unified representation [48], or using spatial as well as appearance information [106].”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [48]
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Citations
Cites
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557 · cited 2×
“BRIEF can achieve matching performances approaching those of scale invariant feature transform (SIFT) of SURF, but without the requirement of a GPU for frame rate operation.”
MethodSimultaneous localization and mapping (SLAM): part II
Bailey, T., & Durrant-Whyte, H. (2006) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2006.1678144 · Cited by 2,554
Faster and Better: A Machine Learning Approach to Corner Detection
Rosten, E., Porter, R. B., & Drummond, T. W. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.275 · Cited by 1,852
SeqSLAM: 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
BRIEF: Computing a Local Binary Descriptor Very Fast
Calonder, M., Lepetit, V., Özuysal, M., Trzcinski, T. P., Strecha, C., & Fua, P. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.222 · Cited by 867
Large-Scale Visual Odometry for Rough Terrain
Konolige, K., Agrawal, M., & Solà, J. (2010) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-14743-2_18 · Cited by 373
Dynamic Maps for Long-Term Operation of Mobile Service Robots
Biber, P., & Duckett, T. (2005) · Robotics: Science and Systems · doi:10.15607/rss.2005.i.003 · Cited by 344
Persistent Navigation and Mapping using a Biologically Inspired SLAM System
Milford, M. J., & Wyeth, G. F. (2009) · The International Journal of Robotics Research · doi:10.1177/0278364909340592 · Cited by 291
Highly scalable appearance-only SLAM - FAB-MAP 2.0
Cummins, M., & Newman, P. M. (2009) · Robotics: Science and Systems · doi:10.15607/rss.2009.v.039 · Cited by 283
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 263
Research works printed but not linked: 2
- Borrmann D, 2010, IEEE International Conference on Intelligent Robots and Systems (IROS) - Workshop on Robotics for Environmental Monitoring
- Konolige K, 2007, International Symposium on Research in Robotics (ISRR)
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Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596
Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I. D., & Leonard, J. J. (2016) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-138892 · Cited by 3,094
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-citationUniversity of Michigan North Campus long-term vision and lidar dataset
Carlevaris-Bianco, N., Ushani, A. K., & Eustice, R. M. (2016) · The International Journal of Robotics Research · doi:10.1177/0278364915614638 · Cited by 576
Maplab: An Open Framework for Research in Visual-Inertial Mapping and Localization
Schneider, T., Dymczyk, M., Fehr, M., Egger, K., Lynen, S., Gilitschenski, I., & Siegwart, R. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2800113 · Cited by 265
Metadata
- Authors
- Churchill, W., & Newman, P.
- Affiliations
- University of Oxford (GB)
- ORCID
- Newman, Paul
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2013-09-16
- Volume
- 32
- Issue
- 14
- Pages
- 1645-1661
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2003919215
- Cited by
- 225
- References
- 23
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 4 · 2023: 15 · 2022: 22 · 2021: 24 · 2020: 23 · 2019: 33 · 2018: 20 · 2017: 17 · 2016: 27 · 2015: 24 · 2014: 2
- FWCI
- 28.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The International Journal of Robotics Research (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/J012017/1, EP/I005021/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- visual odometry, pose estimation, world model learning, time-varying environments, incremental learning
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