Wi-Fi Based Indoor Positioning System For Mobile Robots By Using Particle Filter

TL;DR

This paper presents a Wi-Fi and odometer data fusion system using particle filters for indoor mobile robot positioning, achieving sub-meter accuracy in real-time environments.

Contribution

It introduces a novel indoor positioning method combining Wi-Fi RSS and odometry data with particle filtering, demonstrating improved accuracy over traditional methods.

Findings

Average location error of 0.7606 m with 300 particles.

Average location error of 0.1495 m with 1000 particles.

Effective real-time indoor positioning for mobile robots.

Abstract

Mobile robots have the capability to work in real-time autonomously. Autonomous behavior is strictly dependent on knowing the position of the mobile robot. The positioning of a mobile robot in an indoor area is a difficult task for only one sensor information is used. We proposed a system and method to locate the mobile robot via fusing signals from WIFI and odometer data via particle filter. In this study, the Particle filter is a well-known filter that is used for indoor positioning of mobile robots. The proposed system includes two parts that are RFKON system and evarobot for data collection and experiments. The Received Signal Strength (RSS) measurements of the WiFi access points that are located in any environment are used to locate a stationary mobile robot in one floor area via SIS Particle Filter. RSS measurements from the RFKON database are used and the average location error is 0.7606 and 0.1495 m for 300 and 1000 particles respectively.