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
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Hu, S., Xu, D., & Zhao, H. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341015
Diagnosing Deep Self-localization Network for Domain-shift Localization
Kanji, T. (2021) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/ieeeconf49454.2021.9382702
Fault-Diagnosing Deep-Visual-SLAM for 3D Change Object Detection
Kanji, T. (2021) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2021.p0356
Kanji, T. (2021) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci50451.2021.9660079
Active Map-Matching: Active Self-localization by VPR-to-NBV Knowledge TransferPreprint
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Long-Term LiDAR Place Recognition Based on Phase Congruency in Changing Environments
Cao, F., Li, C., & Wang, T. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10240582
Low-Cost Domain Adaptive Experience Based Localization for Autonomous Robots
Kumar, U., Kala, R., & Nandi, G. C. (2023) · Advances in Robotics - International Conference of The Robotics Society (AIR) · doi:10.1145/3610419.3610430
Chapter 7. Autonomous Navigation for Inspection and Maintenance Ground Robotics
Ivarsen, P. Ø., Keller, J., Sramota, J., Reijgwart, V., Gerhardsen, M. E., Cadena, C., Lundkvist, H., & Moore, R. J. D. (2024) · doi:10.1561/9781638282839.ch7
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Xiao, R., Chen, Y., Zhang, Y. J., Shao, Q., Chen, Y., Han, Y., Lou, Y., & Hu, L. (2026) · IEEE Transactions on Robotics · doi:10.1109/tro.2026.3699433
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Yan, C., Feng, Z., Ye, F., Sun, Y., Gong, Y., & Liu, Y. (2026) · Sensors · doi:10.3390/s26196242
Language Enabled Hierarchical Scene Graphs For Precision Agriculture Autonomy
Mukuddem, A., Speed-Andrews, A., & Amayo, P. (2026) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57385.2026.11695996
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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