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A general approach for egomotion estimation with omnidirectional images
Abstract
Computing a camera's ego-motion from an image sequence is easier to accomplish when a spherical retina is used, as opposed to a standard retinal plane. On a spherical field of view both the focus of expansion and contraction are visible, whereas for a planar retina that is not necessarily the case. Recent research has shown that omnidirectional systems can be used to emulate spherical retinas by mapping image velocity vectors from the omnidirectional image to the spherical retina. That has been done by using the Jacobian of the transformation between the image formation model and the spherical coordinate system. As a consequence, the Jacobian matrix must be derived for each specific omnidirectional camera, to account for the different mirror shapes. Instead, in this paper we derive the Jacobian matrix using of a general projection model, that can describe all single projection center cameras by suitable parameterization. Hence, both the back-projection of an image point to the unit sphere, as well as the mapping of velocities through the transformation Jacobian remains general for all cameras with a single center of projection. We have conducted a series of experimental tests to illustrate the validity of our approach which lead to encouraging results.
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Research works printed but not linked: 20
- matsumoto, 2000, Exploration and navigation in corridor environment based on omni-view sequence, Proc IROS 2000
- coombs, 1995, Real-time obstacle avoidance using central flow divergence and peripherical flow, Proc of ICCV'95
- lee, 2000, Large motion estimation for omnidirectional vision, Proc IEEE Workshop Omnidirectional Vision
- svoboda, 1998, Motion estimation using central panoramic cameras, IEEE Conf on Intelligent Vehicles
- 10.1109/ICCV.1998.710838 · DOI
- 10.1109/OMNVIS.2000.853812 · DOI
- shi, 1994, Good features to track, Proc of CVPR
- lucas, 1981, An iterative image resgistration technique with an application to stereo vision, IJCAI'81
- 10.1016/S0734-189X(83)80026-7 · DOI
- gaspar, 2001, Inter-active reconstruction from an omnidirectional image, Proc of the 9th International Symposium on Intelligent Robotic Systems - SIRS2001
- 10.1109/34.615451 · DOI
- 10.1126/science.270.5244.1973 · DOI
- 10.1007/BF00364131 · DOI
- 10.1109/CVPR.1993.340990 · DOI
- 10.1023/A:1008128724364 · DOI
- 10.1109/ICCV.1998.710698 · DOI
- 10.1007/BF00128130 · DOI
- lustman, 1987, Motion and structure from motion from point and line matches, Proc of First Int'l Conf Computer Vision
- 10.1109/OMNVIS.2000.853799 · DOI
- 10.1109/CVPR.1996.517091 · DOI
Cited by
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Metadata
- Authors
- Vassallo, R. F., Santos-Victor, J., & Schneebeli, H. J.
- Affiliations
- Instituto Superior Técnico (PT)
- Universidade Federal do Espírito Santo (BR)
- Venue
- Proceedings of the IEEE Workshop on Omnidirectional Vision 2002. Held in conjunction with ECCV'02
- Publisher
- IEEE Comput. Soc
- Published
- 2003-06-25
- Pages
- 97-103
- OpenAlex
- W2150385621
- Cited by
- 52
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 1 · 2022: 2 · 2016: 2 · 2015: 2 · 2014: 7 · 2013: 4 · 2012: 2
- FWCI
- 2.71
- Type
- conference paper
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- camera motion estimation, omnidirectional images, focus of expansion, Jacobian matrix, image formation model
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- The paper
- Vassallo, R. F., Santos-Victor, J., & Schneebeli, H. J. (2003). A general approach for egomotion estimation with omnidirectional images. Proceedings of the IEEE Workshop on Omnidirectional Vision 2002. Held in conjunction with ECCV'02. https://doi.org/10.1109/omnvis.2002.1044502
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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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