An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Modern Information Retrieval
Mobile Robot Localization and Map Building: A Multisensor Fusion Approach
Mobile Robot Localization and Map Building: A Multisensor Fusion Approach
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Anytime merging of appearance-based maps
Autonomous Robots
| Hi-index | 0.00 |
Detecting already-visited regions in vision-based navigation and mapping helps reduce drift and position uncertainties. Inspired from content-based image retrieval, an efficient approach is the use of visual vocabularies for measuring similarities between images. In this way, images corresponding to the same scene region can be associated. The state of the art proposals that address this topic suffer from two main drawbacks: (i) they require heavy user intervention, generally involving trial and error tasks for training and parameter tuning and (ii) they are suitable for batch processing only, where all the data is readily available before data processing. We propose a novel method for visual vocabulary navigation and mapping that overcomes these shortcomings. First, the vocabularies are built and updated online, during robot navigation, in order to efficiently represent the visual information present in the scene. Also, the vocabulary building process does not require any user intervention.