The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Visual teach and repeat for long‐range rover autonomy
Abstract
This paper describes a system built to enable long‐range rover autonomy using a stereo camera as the only sensor. During a learning phase, the system builds a manifold map of overlapping submaps as it is piloted along a route. The map is then used for localization as the rover repeats the route autonomously. The use of local submaps allows the rover to faithfully repeat long routes without the need for an accurate global reconstruction. Path following over nonplanar terrain is handled by performing localization in three dimensions and then projecting this down to a local ground plane associated with the current submap to perform path tracking. We have tested this system in an urban area and in a planetary analog setting in the Canadian High Arctic. More than 32 km was covered—99.6% autonomously—with autonomous runs ranging from 45 m to 3.2 km, all without the use of the global positioning system (GPS). Because it enables long‐range autonomous behavior in a single command cycle, visual teach and repeat is well suited to planetary applications, such as Mars sample return, in which no GPS is available. © 2010 Wiley Periodicals, Inc.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
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
Simultaneous localization and mapping: part I
Durrant-Whyte, H., & Bailey, T. (2006) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2006.1638022 · Cited by 4,199
Closed-form solution of absolute orientation using unit quaternions
Horn, B. K. P. (1987) · Journal of the Optical Society of America A · doi:10.1364/josaa.4.000629 · Cited by 4,116
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
Cummins, M., & Newman, P. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364908090961 · Cited by 1,472
Nister, D., Naroditsky, O., & Bergen, J. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315094 · Cited by 1,030
Obstacle avoidance and navigation in the real world by a seeing robot rover
Moravec, H. (2018) · KiltHub Repository · doi:10.1184/r1/6557033 · Cited by 842
Visual Navigation for Mobile Robots: A Survey
Bonin-Font, F., Ortiz, A., & Oliver, G. (2008) · Journal of Intelligent and Robotic Systems · doi:10.1007/s10846-008-9235-4 · Cited by 697
Two years of Visual Odometry on the Mars Exploration Rovers
Maimone, M., Cheng, Y., & Matthies, L. (2007) · Journal of Field Robotics · doi:10.1002/rob.20184 · Cited by 655 · cited 1×
“We use the frame-to-frame VO method described by Maimone, Cheng, and Matthies (2007).”
MethodFrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping
Konolige, K., & Agrawal, M. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2008.2004832 · Cited by 532
Cited by
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
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
Bresson, G., Alsayed, Z., Yu, L., & Glaser, S. (2017) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2749181 · Cited by 885
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
Artificial Intelligence for Long-Term Robot Autonomy: A Survey
Kunze, L., Hawes, N., Duckett, T., Hanheide, M., & Krajnik, T. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2860628 · Cited by 191
Ving: Learning open-world navigation with visual goals
Shah, D., Eysenbach, B., Kahn, G., Rhinehart, N., & Levine, S. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561936 · Cited by 81
Tinchev, G., Penate-Sanchez, A., & Fallon, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2895264 · Cited by 51
“Robot localization has been heavily explored using different sensors such as LIDAR [10], vision [11], GPS [12] or”
MethodPaton, 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
Self-citationTapered whisker reservoir computing for real-time terrain identification-based navigation
Yu, Z., Sadati, S. M. H., Perera, S., Hauser, H., Childs, P. R. N., & Nanayakkara, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-31994-x · Cited by 21
Metadata
- Authors
- Furgale, P., & Barfoot, T. D.
- Venue
- Journal of Field Robotics
- Publisher
- Wiley
- Published
- 2010-08-03
- Volume
- 27
- Issue
- 5
- Pages
- 534-560
- ISSN
- 1556-4959 (ISSN-L)
- 1556-4967
- OpenAlex
- W4230475428
- Cited by
- 266
- References
- 40
- FWCI
- 18.78
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
