It's not easy seeing green: Lighting-resistant stereo Visual Teach & Repeat using color-constant images
Abstract
Stereo Visual Teach & Repeat (VT&R) is a system for long-range, autonomous route following in unstructured 3D environments. As this system relies on a passive sensor to localize, it is highly susceptible to changes in lighting conditions. Recent work in the optics community has provided a method to transform images collected from a three-channel passive sensor into color-constant images that are resistant to changes in outdoor lighting conditions. This paper presents a lighting-resistant VT&R system that uses experimentally trained color-constant images to autonomously navigate difficult outdoor terrain despite changes in lighting. We show through an extensive field trial that our algorithm is capable of autonomously following a 1km outdoor route spanning sandy/rocky terrain, grassland, and wooded areas. Using a single visual map created at midday, the route was autonomously repeated 26 times over a period of four days, from sunrise to sunset with an autonomy rate (by distance) of over 99.9%. These experiments show that a simple image transformation can extend the operation of VT&R from a few hours to multiple days.
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Stereo camera systems, as well as 3D lidars, have been used to determine the 3D structure of roads directly.
“Stereo camera systems [207], as well as 3D lidars [204], have been used determine the 3D structure of roads directly.”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [204]
Color statistics and intensity information, gradient information, and various other filters have been used to detect lane markings.
“Color statistics and intensity information [204], gradient information [205], and various other filters have been used to detect lane markings.”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [204]
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 1× · see citing works
“To autonomously repeat a path, VT&R relies on extracting SURF [1] features from greyscale images, which are typically constructed using the green channel in an RGB camera.”
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 · see citing works
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A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i>
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889 · see citing works
“this usually means performing image color corrections to normalize lighting conditions [200].”
Background
- Authors
- Paton, M., MacTavish, K., Ostafew, C. J., & Barfoot, T. D.
- Affiliations
- Beijing Institute of Technology (CN)
- University of Nevada, Reno (US)
- Venue
- 2015 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 1519-1526
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1562320275
- Cited by
- 30
- References
- 2
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 2 · 2021: 3 · 2020: 4 · 2019: 6 · 2018: 2 · 2017: 3 · 2016: 6 · 2015: 2
- FWCI
- 7.51
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- passive vision, autonomous navigation, outdoor terrain, visual localization, image transformation, field trials
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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