Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Digital Image Processing
Journal of Intelligent and Robotic Systems
Exploring artificial intelligence in the new millennium
Towards a general theory of topological maps
Artificial Intelligence
Effective maximum likelihood grid map withconflict evaluation filter using sonar sensors
IEEE Transactions on Robotics
SLAM in large indoor environments with low-cost, noisy, and sparse sonars
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Topological modeling and classification in home environment using sonar gridmap
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
incremental topological modeling using sonar gridmap in home environment
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Topological localization with kidnap recovery using sonar grid map matching in a home environment
Robotics and Computer-Integrated Manufacturing
Topological map induction using neighbourhood information of places
Autonomous Robots
Evolutionary computation for intelligent self-localization in multiple mobile robots based on SLAM
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part I
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This paper presents a method of autonomous topological modeling and localization in a home environment using only low-cost sonar sensors. The topological model is extracted from a grid map using cell decomposition and normalized graph cut. The autonomous topological modeling involves the incremental extraction of a subregion without predefining the number of subregions. A method of topological localization based on this topological model is proposed wherein a current local grid map is compared with the original grid map. The localization is accomplished by obtaining a node probability from a relative motion model and rotational invariant grid-map matching. The proposed method extracts a well-structured topological model of the environment, and the localization provides reliable node probability even when presented with sparse and uncertain sonar data. Experimental results demonstrate the performance of the proposed topological modeling and localization in a real home environment.