Mobile Robot Localization Using Sonar
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Selecting Landmarks for Localization in Natural Terrain
Autonomous Robots
IEEE Intelligent Systems
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
IEEE Transactions on Robotics
Assistive robot application based on an RFID control architecture and a wireless EOG interface
Robotics and Autonomous Systems
An RFID-based manufacture process control and supply chain management in the semiconductor industry
International Journal of Information Technology and Management
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This work is concerned with the problem of robot localization using standard RFID tags as landmarks and an RFID reader as a landmark sensor. A main advantage of such an RFID-based localization system is the availability of landmark ID measurement, which trivially solves the data association problem. While the main drawback of an RFID system is its low spatial accuracy. The result in this paper is an improvement of the localization accuracy for a standard short-range RFID sensor. One of the main contributions is a proposal of a machine learning approach in which multiple classifiers are trained to distinguish RFID-signal features of each location. Another contribution is a design tool for tag arrangement by which the tag configuration needs not be manually designed by the user, but can be automatically recommended by the system. The effectiveness of the proposed technique is evaluated experimentally with a real mobile robot and an RFID system.