Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision-Based SLAM: Stereo and Monocular Approaches
International Journal of Computer Vision
Natural landmark extraction for mobile robot navigation based on an adaptive curvature estimation
Robotics and Autonomous Systems
Perception-based image segmentation using the bounded irregular pyramid
Proceedings of the 29th DAGM conference on Pattern recognition
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
Bayesian inference in the space of topological maps
IEEE Transactions on Robotics
Hi-index | 0.00 |
Hybrid maps combine metric and topological information for efficiently managing large-scale environments. In a feature-based mapping framework, this paper describes the application of a spectral clustering approach for automatically detecting the transitions between subsequently traversed local maps. Contrary to recently proposed approaches, this algorithm considers each individual map feature as a node of a graph whose edges link two nodes if they are simultaneously observed. Thus, given a sequence of observations, an auxiliary graph is incrementally built whose 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 mapping approach, the feature-based graph is split into two subgraphs using a normalized spectral clustering algorithm. If the graph partition is validated, the algorithm determines that the robot is moving into a new area and a new local map is generated. We have tested the proposed approach in real environments where features are obtained using 2D laser sensors or vision. Experimental results demonstrate the performance of the proposal.