A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Omnidirectional Vision Based Topological Navigation
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
The New College Vision and Laser Data Set
International Journal of Robotics Research
A robust Graph Transformation Matching for non-rigid registration
Image and Vision Computing
Graph-based robust shape matching for robotic application
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Rapid and precise object detection based on color histograms and adaptive bandwidth mean shift
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Visual place categorization: problem, dataset, and algorithm
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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Image feature extraction and matching is useful in many areas of robotics such as object and scene recognition, autonomous navigation, SLAM and so on. This paper describes a new approach to the problem of matching features and its application to scene recognition and topological SLAM. For that purpose we propose a prior image segmentation into regions in order to group the extracted features in a graph so that each graph defines a single region of the image. We compare two basic methods for image segmentation, in order to know the effect of segmentation in the result. We have also extend the initial segmentation algorithm in order to take into account the circular characteristics of the omnidirectional image. The matching process will take into account the features and the structure (graph) using the GTM algorithm, modified to take into account the cylindrical structure of omnidirectional images. Then, using this method of comparing images, we propose an algorithm for constructing topological maps. During the experimentation phase we will test the robustness of the method and its ability to construct topological maps. We have also introduced a new hysteresis behavior in order to solve some problems found in the graph construction.