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Crowdsensing Indoor Walking Paths with Massive Noisy Crowdsourcing User Traces

Crowdsensing indoor walking paths based on crowdsourcing traces collected from normal users has recently become an emerging topic for indoor positioning, which can reduce the labor effort of building radio maps and improve positioning accuracy when a floor plan is unavailable. In this work, we design an indoor walking path crowdsensing system with massive noisy crowdsourcing traces. In this system, we propose a robust iterative trace merging algorithm based on WiFi access points as markers (named 'WiFi-RITA') to merge massive noisy traces. The algorithm formulates the trace merging problem as an optimization problem in which each trace is controlled to translate and rotate to minimize the limitation of distances among traces defined by WiFi access points as markers. WiFi-RITA is robust to the rotation errors and uncertain absolute locations of user traces, and can efficiently work for a large number of user traces. We further adopt a landmark matching algorithm to match the merged traces to the target building and take a 2- dimensional histogram approach to remove outlier traces. With such procedures, we generate walking paths of a large-scale building with a mean accuracy of 2.1m.