On Handling Uncertainty in the Fundamental Matrix for Scene and Motion Adaptive Pose Recovery
Abstract
The estimation of the fundamental matrix is the key step in feature-based camera ego-motion estimation for applications in scene modeling and vehicle navigation. In this paper, we present a new method of analyzing and further reducing the risk in the fundamental matrix due to the choice of a particular feature detector, the choice of the matching algorithm, the motion model, iterative hypothesis generation and verification paradigms. Our scheme makes use of model-selection theory to guide the switch to optimal methods for fundamental matrix estimation within the hypothesis-and-test architecture. We demonstrate our proposed method for vision-based robot localization in large-scale environments where the environment is constantly changing and navigation within the environment is unpredictable.
Cite
Text
Sukumar et al. "On Handling Uncertainty in the Fundamental Matrix for Scene and Motion Adaptive Pose Recovery." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587567Markdown
[Sukumar et al. "On Handling Uncertainty in the Fundamental Matrix for Scene and Motion Adaptive Pose Recovery." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/sukumar2008cvpr-handling/) doi:10.1109/CVPR.2008.4587567BibTeX
@inproceedings{sukumar2008cvpr-handling,
title = {{On Handling Uncertainty in the Fundamental Matrix for Scene and Motion Adaptive Pose Recovery}},
author = {Sukumar, Sreenivas R. and Bozdogan, Hamparsum and Page, David L. and Koschan, Andreas F. and Abidi, Mongi A.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2008},
doi = {10.1109/CVPR.2008.4587567},
url = {https://mlanthology.org/cvpr/2008/sukumar2008cvpr-handling/}
}