2009. 7. 23. 18:53
Computer Vision
Brian Williams, Georg Klein and Ian Reid
(Department of Engineering Science, University of Oxford, UK)
Real-Time SLAM Relocalisation
In Proceedings of the International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007
demo 1
demo 2
Lepetit's image patch classifier (feature appearance learning)
=> integrating the classifier more closely into the process of map-building
(by using classification results to aid in the selection of new points to add to the map)
> recovery from tracking failure: local vs. global
local - particle filter -> rich feature descriptor
global - proximity using previous key frames
- based on SceneLib (Extended Kalman Filter)
- rotational (and a degree of perspective) invariance via local patch warping
- assuming the patch is fronto-parallel when first seen
http://freshmeat.net/projects/scenelib/
active search
innovation covariance
joint compatibility test
training the classifier
The RANSAC (Random Sample Consensus) Algorithm
ref.
Davison, A. J. and Molton, N. D. 2007.
MonoSLAM: Real-Time Single Camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29, 6 (Jun. 2007), 1052-1067. DOI= http://dx.doi.org/10.1109/TPAMI.2007.1049
Vision-based global localization and mapping for mobile robots
Se, S. Lowe, D.G. Little, J.J. (MD Robotics, Brampton, Ont., Canada)
Lepetit, V. 2006.
Keypoint Recognition Using Randomized Trees. IEEE Trans. Pattern Anal. Mach. Intell. 28, 9 (Sep. 2006), 1465-1479. DOI= http://dx.doi.org/10.1109/TPAMI.2006.188
Lepetit, V., Lagger, P., and Fua, P. 2005.
Randomized Trees for Real-Time Keypoint Recognition. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cvpr'05) - Volume 2 - Volume 02 (June 20 - 26, 2005). CVPR. IEEE Computer Society, Washington, DC, 775-781. DOI= http://dx.doi.org/10.1109/CVPR.2005.288
Fischler, M. A. and Bolles, R. C. 1981.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 6 (Jun. 1981), 381-395. DOI= http://doi.acm.org/10.1145/358669.358692
(Department of Engineering Science, University of Oxford, UK)
Real-Time SLAM Relocalisation
In Proceedings of the International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007
williams_etal_iccv2007.pdfdemo 1
demo 2
• real-time, high-accuracy localisation and mapping during tracking
• real-time (re-)localisation when when tracking fails
• on-line learning of image patch appearance so that no prior training or map structure is required and features are added and removed during operation.
• real-time (re-)localisation when when tracking fails
• on-line learning of image patch appearance so that no prior training or map structure is required and features are added and removed during operation.
Lepetit's image patch classifier (feature appearance learning)
=> integrating the classifier more closely into the process of map-building
(by using classification results to aid in the selection of new points to add to the map)
> recovery from tracking failure: local vs. global
local - particle filter -> rich feature descriptor
global - proximity using previous key frames
- based on SceneLib (Extended Kalman Filter)
- rotational (and a degree of perspective) invariance via local patch warping
- assuming the patch is fronto-parallel when first seen
http://freshmeat.net/projects/scenelib/
active search
innovation covariance
joint compatibility test
randomized lists key-point recognition algorithm
1. randomized: (2^D - 1) tests -> D tests
2. independent treatment of classes
3. binary leaf scores (2^D * C * N bits for all scores)
4. intensity offset
5. explicit noise handing
1. randomized: (2^D - 1) tests -> D tests
2. independent treatment of classes
3. binary leaf scores (2^D * C * N bits for all scores)
4. intensity offset
5. explicit noise handing
training the classifier
The RANSAC (Random Sample Consensus) Algorithm
ref.
Davison, A. J. and Molton, N. D. 2007.
MonoSLAM: Real-Time Single Camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29, 6 (Jun. 2007), 1052-1067. DOI= http://dx.doi.org/10.1109/TPAMI.2007.1049
Vision-based global localization and mapping for mobile robots
Se, S. Lowe, D.G. Little, J.J. (MD Robotics, Brampton, Ont., Canada)
Lepetit, V. 2006.
Keypoint Recognition Using Randomized Trees. IEEE Trans. Pattern Anal. Mach. Intell. 28, 9 (Sep. 2006), 1465-1479. DOI= http://dx.doi.org/10.1109/TPAMI.2006.188
Lepetit, V., Lagger, P., and Fua, P. 2005.
Randomized Trees for Real-Time Keypoint Recognition. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cvpr'05) - Volume 2 - Volume 02 (June 20 - 26, 2005). CVPR. IEEE Computer Society, Washington, DC, 775-781. DOI= http://dx.doi.org/10.1109/CVPR.2005.288
Fischler, M. A. and Bolles, R. C. 1981.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 6 (Jun. 1981), 381-395. DOI= http://doi.acm.org/10.1145/358669.358692
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