The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Real-time stereo visual odometry for autonomous ground vehicles
Abstract
This paper describes a visual odometry algorithm for estimating frame-to-frame camera motion from successive stereo image pairs. The algorithm differs from most visual odometry algorithms in two key respects: (1) it makes no prior assumptions about camera motion, and (2) it operates on dense disparity images computed by a separate stereo algorithm. This algorithm has been tested on many platforms, including wheeled and legged vehicles, and has proven to be fast, accurate and robust. For example, after 4000 frames and 400 m of travel, position errors are typically less than 1 m (0.25% of distance traveled). Processing time is approximately 20 ms on a 512times384 image. This paper includes a detailed description of the algorithm and experimental evaluation on a variety of platforms and terrain types.
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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
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
A Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 1×
“The approach described in this paper, however, does not derive from the Matthies/MER/MSL lineage.”
Machine Learning for High-Speed Corner Detection
Rosten, E., & Drummond, T. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_34 · Cited by 4,555
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×
“Visual odometry, which estimates vehicle motion from a sequence of camera images, offers a natural complement to these sensors: it is insensitive to soil mechanics, produces a full 6DOF motion estimate, and has lower drift rates than all but the most expensive IMUs.”
Background
Cited by
Scaramuzza, D., & Fraundorfer, F. (2011) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2011.943233 · Cited by 1,550
RGB-D camera based wearable navigation system for the visually impaired
Lee, Y. H., & Medioni, G. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.03.019 · Cited by 143
Towards visual ego-motion learning in robots
Pillai, S., & Leonard, J. J. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206441 · Cited by 42
Metadata
- Authors
- Howard, A.
- Venue
- 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
- Publisher
- IEEE
- Published
- 2008-09
- Pages
- 3946-3952
- OpenAlex
- W2097197771
- Cited by
- 383
- References
- 7
- FWCI
- 41.07
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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