Device diversity in crowdsourced WiFi fingerprinting database for autonomous mobile robot

摘要

Positioning and navigation of mobile robot is the main feature for the trajectory or motion of the mobile robot. Conventional mobile robot positioning and navigation system relies heavily on fusion of multiple costly sensors, which does not promote mass production. This paper aim is to use readily and available technologies which is WiFi due to its reliability as it is pre-deployed, and it exist in most of the building. The system used are based on indoor positioning system (IPS) by using a crowdsourced fingerprinting method. This seeks to improve crowdsourced fingerprinting database performance by solving the issue of the device diversity or heterogeneity of difference devices. To cope with the crowdsourced fingerprinting database as the location estimation method for the robot application, deep neural network (DNN) is employed. The proposed method namely ratio and ranged-based (RRB) shows an improvement of 60% increments by implementing the pre-processing technique of the raw data before feeding it to the DNN. The comparison between other method shows that RRB is better in term of accuracy in three validation techniques, which are root mean square error (RMSE), distance error and accuracy between true and estimate position. This improvement effectively could facilitate the actual positioning system utilizing the WiFi infrastructure for the mobile robot in very near future.