Crowdsensing Indoor Walking Paths with Massive Noisy Crowdsourcing User Traces

Li, Zan; Zhao, Xiaohui; Zhao, Zhongliang; Hu, Fengye; Liang, Hui; Braun, Torsten (30 April 2018). Crowdsensing Indoor Walking Paths with Massive Noisy Crowdsourcing User Traces. In: IEEE Global Communications Conference (GLOBECOM). Abu Dhabi, UAE. 9-13 December 2018.

Text final version.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (652kB) | Request a copy

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 the 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 adopt 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.

Interest & Impact

Downloads

6 since deposited on 30 Apr 2018
0 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item