Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Active-semantic localization with a single consumer-grade camera
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words
IEEE Transactions on Robotics
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Autonomous mobile robots need environmental maps to navigate to specific destinations, but there are difficulties in generating and acquiring efficient maps for them. Map learning systems and map representation for autonomous robot navigation are highly interrelated and need a total system design that combines these two factors. This study considers a combined simple map representation and map learning system. The proposed map representation includes geometrical relationships between important places and grid maps for these places, but not a total grid map of the environment. In particular, the study focuses on the ability to recognize places based on image features. Successful experiments on autonomous navigation with the proposed map representation using an actual mobile robot are described.