Topological mapping for mobile robots using a combination of sonar and vision sensing
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Contextual Priming for Object Detection
International Journal of Computer Vision
Bootstrap learning for place recognition
Eighteenth national conference on Artificial intelligence
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object Recognition Using Composed Receptive Field Histograms of Higher Dimensionality
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Class-Specific Material Categorisation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Supervised semantic labeling of places using information extracted from sensor data
Robotics and Autonomous Systems
Conceptual spatial representations for indoor mobile robots
Robotics and Autonomous Systems
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Mobile localization in outdoor environments
WOWMOM '08 Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks
Cue integration through discriminative accumulation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
IEEE Transactions on Robotics
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Combining image invariant features and clustering techniques for visual place classification
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
An extended-HCT semantic description for visual place recognition
International Journal of Robotics Research
Robotics and Autonomous Systems
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An important competence for a mobile robot system is the ability to localize and perform context interpretation. This is required to perform basic navigation and to facilitate local specific services. Recent advances in vision have made this modality a viable alternative to the traditional range sensors, and visual place recognition algorithms emerged as a useful and widely applied tool for obtaining information about robot's position. Several place recognition methods have been proposed using vision alone or combined with sonar and/or laser. This research calls for standard benchmark datasets for development, evaluation and comparison of solutions. To this end, this paper presents two carefully designed and annotated image databases augmented with an experimental procedure and extensive baseline evaluation. The databases were gathered in an uncontrolled indoor office environment using two mobile robots and a standard camera. The acquisition spanned across a time range of several months and different illumination and weather conditions. Thus, the databases are very well suited for evaluating the robustness of algorithms with respect to a broad range of variations, often occurring in real-world settings. We thoroughly assessed the databases with a purely appearance-based place recognition method based on support vector machines and two types of rich visual features (global and local).