From Fast to Accurate Wireless Map Reconstruction for Human Positioning Systems

Abstract

Indoor localization systems for humans are becoming commonplace for context-aware applications. In many public areas such as shopping malls or airports, existing wireless infrastructures can be used for localization, often through approaches based on fingerprinting. Although those systems do not require additional installation, a previous calibration phase is needed. This calibration task becomes tedious and time consuming for large scenarios, since the wireless signal must be measured in many different locations. This paper proposes an algorithm to perform this wireless map calibration autonomously by means of a robot. Instead of sampling thoroughly the full scenario from the beginning, our algorithm fosters a more sensible behavior when the calibration time may be limited: first, the robot tries to explore all areas to gain an overall view of the map; and then, it improves the accuracy by sampling more deeply each sector if there is remaining time. For this purpose, full coverage of individual rooms is ranked lower if others are still unexplored. Moreover, we propose some metrics to evaluate this kind of behavior and evaluate our exploration algorithm against a traditional coverage system in two different simulated scenarios.