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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New methods have been developed to account for illumination variations by matching sequences, gathering different visual appearances into a unified representation, or using spatial and appearance information.
“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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Paton, M., MacTavish, K., Ostafew, C. J., & Barfoot, T. D. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139391 · Cited by 30 · see citing works
Appearance-based landmark selection for efficient long-term visual localization
Bürki, M., Gilitschenski, I., Stumm, E. S., Siegwart, R., & Nieto, J. I. (2016) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-010819567 · Cited by 26 · see citing works
Place Recognition: An Overview of Vision Perspective
Zeng, Z., Zhang, J., Wang, X., Chen, Y., & Zhu, C. (2018) · Applied Sciences · doi:10.3390/app8112257 · Cited by 25 · see citing works
Adversarial Feature Disentanglement for Place Recognition Across Changing Appearance
Tang, L., Wang, Y., Luo, Q., Ding, X., & Xiong, R. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196518 · Cited by 24 · see citing works
ROLL: Long-Term Robust LiDAR-based Localization With Temporary Mapping in Changing Environments
Peng, B., Xie, H., & Chen, W. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9982153 · Cited by 24 · see citing works
Visual triage: A bag-of-words experience selector for long-term visual route following
MacTavish, K., Paton, M., & Barfoot, T. D. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989238 · Cited by 23 · see citing works
VIZARD: Reliable Visual Localization for Autonomous Vehicles in Urban Outdoor Environments
Bürki, M., Schaupp, L., Dymczyk, M., Dubé, R., Cadena, C., Siegwart, R., & Nieto, J. I. (2019) · 2019 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2019.8814017 · Cited by 25 · see citing works
Collaborative Augmented Reality on Smartphones via Life-long City-scale Maps
Platinsky, L., Szabados, M., Hlasek, F., Hemsley, R., Pero, L. D., Pancik, A., Baum, B., Grimmett, H., & Ondruska, P. (2020) · International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar50242.2020.00081 · Cited by 20 · see citing works
BioSLAM: A Bioinspired Lifelong Memory System for General Place Recognition
Yin, P., Abuduweili, A., Zhao, S., Xu, L., Liu, C., & Scherer, S. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3306615 · Cited by 24 · see citing works
Erasing bad memories: Agent-side summarization for long-term mapping
Dymczyk, M., Schneider, T., Gilitschenski, I., Siegwart, R., & Stumm, E. S. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759673 · Cited by 23 · see citing works
I Can See for Miles and Miles: An Extended Field Test of Visual Teach and Repeat 2.0
Paton, M., MacTavish, K., Berczi, L.-P., van Es, S. K., & Barfoot, T. D. (2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-67361-5_27 · Cited by 22 · see citing works
Will It Last? Learning Stable Features for Long-Term Visual Localization
Dymczyk, M., Stumm, E. S., Nieto, J. I., Siegwart, R., & Gilitschenski, I. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.66 · Cited by 21 · see citing works
Contrastive Learning for Image Registration in Visual Teach and Repeat Navigation
Rozsypálek, Z., Broughton, G., Linder, P., Rouček, T., Blaha, J., Mentzl, L., Kusumam, K., & Krajník, T. (2022) · Sensors · doi:10.3390/s22082975 · Cited by 20 · see citing works
Checkout my map: Version control for fleetwide visual localisation
Gadd, M., & Newman, P. M. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759843 · Cited by 18 · see citing works
Self-citationScalable Place Recognition Under Appearance Change for Autonomous DrivingPreprint
Doan, A.-D., Latif, Y., Chin, T.-J., Liu, Y., Do, T.-T., & Reid, I. D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.00178 · Cited by 20 · see citing works
Appearance‐based landmark selection for visual localization
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Life-long spatio-temporal exploration of dynamic environments
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Multi-modal and inertial sensor solutions for navigation-type factor graphs
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Time-varying Pedestrian Flow Models for Service Robots
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Selective memory: Recalling relevant experience for long‐term visual localization
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Off the beaten track: Predicting localisation performance in visual teach and repeat
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Self-citationChange detection using weighted features for image-based localization
Derner, E., Gómez, C., Hernandez, A. C., Barber, R., & Babuška, R. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103676 · Cited by 16 · see citing works
Learning Matchable Image Transformations for Long-Term Metric Visual Localization
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Improving RGB-D SLAM in dynamic environments using semantic aided segmentation
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Multidimensional Particle Filter for Long-Term Visual Teach and Repeat in Changing Environments
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Adaptive Image Processing Methods for Outdoor Autonomous Vehicles
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On the Redundancy Detection in Keyframe-Based SLAM
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Know your limits: Embedding localiser performance models in teach and repeat maps
Churchill, W., Tong, C. H., Gurău, C., Posner, I., & Newman, P. M. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139783 · Cited by 14 · see citing works
Self-citationWrong Today, Right Tomorrow: Experience-Based Classification for Robot Perception
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- 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 › Engineering › Aerospace Engineering › 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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