The spatial semantic hierarchy
Artificial Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Monte Carlo localization in outdoor terrains using multilevel surface maps
Journal of Field Robotics - Special Issue on Field and Service Robotics
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Modelling models of robot navigation using formal spatial ontology
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
Specification of an ontology for route graphs
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
Simplified map representation and map learning system for autonomous navigation of mobile robots
Intelligent Service Robotics
Local map-based exploration for mobile robots
Intelligent Service Robotics
Hi-index | 0.00 |
This study addressed the problem of active localization, which requires massive computation. To solve the problem, we developed abstracted measurements that consist of qualitative metrics estimated by a single camera. These are contextual representations consisting of perceived landmarks and their spatial relations, and they can be shared by humans and robots. Next, we enhanced the Markov localization method to support contextual representations with which a robot's location can be sufficiently estimated. In contrast to passive methodologies, our approach actively uses the greedy technique to select a robot's action and improve localization results. The experiment was carried out in an indoor environment, and results indicate that the proposed active-semantic localization yields more efficient localization.