Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robotics and Autonomous Systems
Natural landmark extraction for mobile robot navigation based on an adaptive curvature estimation
Robotics and Autonomous Systems
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Subjective local maps for hybrid metric-topological SLAM
Robotics and Autonomous Systems
Thin junction tree filters for simultaneous localization and mapping
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A novel approach for salient image regions detection and description
Pattern Recognition Letters
A comparison of loop closing techniques in monocular SLAM
Robotics and Autonomous Systems
CI-graph: an efficient approach for large scale SLAM
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Automatically and efficiently inferring the hierarchical structure of visual maps
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
CI-graph: an efficient approach for large scale SLAM
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Normalized graph cuts for visual SLAM
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
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words
IEEE Transactions on Robotics
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
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Given the features obtained from a sequence of consecutively acquired sensor readings, this paper proposes an on-line algorithm for unsupervisedly detecting a transition on this sequence, i.e. the frame that divides the sequence into two tightly related parts that are dissimilar between them. Contrary to recently proposed approaches that address this partitioning problem dealing with a sequence of robot's poses, our proposal considers each individual feature as a node of an incrementally built graph whose edges link two nodes if their associated features were simultaneously observed. These graph edges carry non-negative weights according to the locality of the features. Given a feature, its locality defines the set of features that has been observed simultaneously with it at least once. At each execution of the algorithm, the feature-based graph is split into two subgraphs using a normalized spectral clustering algorithm. The obtained partitions correspond to those parts in the environment that share the minimum amount of information. If this graph partition is validated, the algorithm determines that there is a significant change on the perceived scenario, assuming that a transition area has been traversed. In a map partitioning framework, we have tested the proposed approach in real environments where features are obtained using 2D laser sensors or vision (stereo and monocular cameras). The statistical evaluation of the experimental results demonstrates the performance of the proposal.