Using Hidden Markov Models to Improve Floor Level Localisation
نویسندگان
چکیده
The focus of this paper is on estimating the floor level of a robot/person moving in a multifloor environment. It demonstrates how information about transitions between floors can be employed within a probabilistic framework to improve the accuracy of floor level estimation. This is achieved by combining a simple linear classifier with a Hidden Markov Model that captures the two basic motion patterns in a multi-floor environment: within-floor and between floors, switching from one to the other as floor transition events are detected. Through real-world experiments, we demonstrate the ability of this framework to produce accurate floor level estimates using only RSSI (Received Signal Strength Indicator) measurements, even when operating in an environment with as little as five WiFi access points per floor.
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