On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
Robot Motion Planning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Bootstrap learning for place recognition
Eighteenth national conference on Artificial intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
Robotics Research: Results of the 12th International Symposium ISRR (Springer Tracts in Advanced Robotics)
Relational object maps for mobile robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Conceptual spatial representations for indoor mobile robots
Robotics and Autonomous Systems
Training conditional random fields using virtual evidence boosting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Visual place categorization: problem, dataset, and algorithm
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Following directions using statistical machine translation
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Detecting region transitions for human-augmented mapping
IEEE Transactions on Robotics
Classification and Semantic Mapping of Urban Environments
International Journal of Robotics Research
Inferring laser-scan matching uncertainty with conditional random fields
Robotics and Autonomous Systems
Histogram of Oriented Uniform Patterns for robust place recognition and categorization
International Journal of Robotics Research
Hierarchical Classifiers for Robust Topological Robot Localization
Journal of Intelligent and Robotic Systems
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The ability to build maps of indoor environments is extremely important for autonomous mobile robots. In this paper we introduce Voronoi random fields (VRFs), a novel technique for mapping the topological structure of indoor environments. Our maps describe environments in terms of their spatial layout along with information about the different places and their connectivity. To build these maps, we extract a Voronoi graph from an occupancy grid map generated with a laser range-finder, and then represent each point on the Voronoi graph as a node of a conditional random field, which is a discriminatively trained graphical model. The resulting VRF estimates the label of each node, integrating features from both the map and the Voronoi topology. The labels provide a segmentation of an environment, with the different segments corresponding to rooms, hallways, or doorways. Experiments using different maps show that our technique is able to label unknown environments based on parameters learned from other environments.