Integrating topological and metroc maps for mobile robot navigation: a statistical approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
Robotics-based location sensing using wireless ethernet
Proceedings of the 8th annual international conference on Mobile computing and networking
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Large multimedia artifacts prebuffering in mobile information systems as location context awareness
ISWPC'09 Proceedings of the 4th international conference on Wireless pervasive computing
WiFi localization system based on fuzzy logic to deal with signal variations
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
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EURASIP Journal on Wireless Communications and Networking - Special issue on enabling Wireless Technologies for Green Pervasive Computing
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EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part II
Information Sciences: an International Journal
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This paper compares two methods to estimate the position of a mobile robot in an indoor environment using only odometric calculus and the WiFi energy received from the wireless communication infrastructure. In both cases we use a well-known probabilistic method based on the Bayes rule to accumulate localization probability as the robot moves on with an experimental WiFi map, and with a theoretically built WiFi map. We will show several experiments made in our university building to compare both methods using a Pioneer robot. The two major contributions of the presented work are that the self-localization error achieved with WiFi energy is bounded, and that no significant degradation is observed when the expected WiFi energy at each point is taken from radio propagation model, instead of an a priori experimental intensity map of the environment.