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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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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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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