A fully projective formulation to improve the accuracy of Lowe's pose-estimation algorithm
Computer Vision and Image Understanding
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Randomized Trees for Real-Time Keypoint Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Monocular Vision for Mobile Robot Localization and Autonomous Navigation
International Journal of Computer Vision
Keypoint Signatures for Fast Learning and Recognition
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Manifold surface reconstruction of an environment from sparse Structure-from-Motion data
Computer Vision and Image Understanding
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Localizing a vehicle with a vision based system often requires to match and track landmarks whose position is known. This paper tries to define a new method to track some features in modeling them as local planar patches with a monocular camera. First a learning sequence is recorded to compute the planar features and their orientation around landmarks tracked on several views. Then in the localization part, camera pose is predicted and features are transformed to fit with the scene as seen by the camera. Landmarks can then easily be matched and position is computed more accurately. With this method many features can be tracked on longer sequences than with standard methods, even if the camera is moving away from the learning trajectory. This improves the localization.