Map-Assisted Seamless Localization Using Crowdsourced Trajectories Data and Bi-LSTM Based Quality Control Criteria

Abstract

Seamless localization service is regarded as an essential requirement towards smart city based applications. The performance of seamless localization is restricted by the limited coverage of indoor navigation database and disturbed results of the location sources in complex urban environments. This paper proposes a map-assisted seamless localization framework that uses the self-constructed navigation database and Bi-directional Long Short-Term Memory (Bi-LSTM) based signal quality control criteria (SL-SDBQ). Daily-life trajectories provided by the inertial odometry are optimized and calibrated by the map-originated indoor pedestrian network information for self-constructing the crowdsourced navigation database, and the Bi-LSTM is applied for quality evaluation of different location sources. In addition, the robust Kalman filter is adopted to integrate the information of inertial odometry, crowdsourced Wi-Fi fingerprinting, Global Navigation Satellite System (GNSS), and signal quality evaluation results, and the error ellipse is applied for map matching to further enhance the performance of multi-source fusion based seamless positioning. The experimental results prove that the proposed SL-SDBQ can achieve autonomous and precise indoor and outdoor positioning performance, and meter-level localization precision can be realized under the assistance of indoor map.