LANDMARC: Indoor Location Sensing Using Active RFID
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Ferret: RFID localization for pervasive multimedia
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
Relational Transformation-based Tagging for Activity Recognition
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Object localization using RFID
ISWPC'10 Proceedings of the 5th IEEE international conference on Wireless pervasive computing
Learning search heuristics for finding objects in structured environments
Robotics and Autonomous Systems
Cryptography and Security
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Journal of Field Robotics
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Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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In recent years, there has been an increasing interest within the robotics community in investigating whether Radio Frequency Identification (RFID) technology can be utilized to solve localization and mapping problems in the context of mobile robots. We present a novel sensor model which can be utilized for localizing RFID tags and for tracking a mobile agent moving through an RFID-equipped environment. The proposed probabilistic sensor model characterizes the received signal strength indication (RSSI) information as well as the tag detection events to achieve a higher modeling accuracy compared to state-of-the-art models which deal with one of these aspects only. We furthermore propose a method that is able to bootstrap such a sensor model in a fully unsupervised fashion. Real-world experiments demonstrate the effectiveness of our approach also in comparison to existing techniques.