Crowd-sensing Simultaneous Localization and Radio Fingerprint Mapping based on Probabilistic Similarity Models

TL;DR

This paper introduces a crowd-sensing SLAM system that uses WiFi signal strengths and smartphone motion data to localize users and map radio fingerprints in large indoor environments without prior maps, achieving around 1.74 meters accuracy.

Contribution

It presents a novel probabilistic similarity model-based approach for crowd-sensing SLAM using WiFi RSS and motion tracking, suitable for unknown indoor spaces.

Findings

Achieved 1.74 meters localization accuracy.

Effective in dynamic indoor environments.

Utilized ubiquitous WiFi signals for SLAM.

Abstract

Simultaneous localization and mapping (SLAM) has been richly researched in past years particularly with regard to range-based or visual-based sensors. Instead of deploying dedicated devices that use visual features, it is more pragmatic to exploit the radio features to achieve this task, due to their ubiquitous nature and the wide deployment of Wifi wireless network. In this paper, we present a novel approach for crowd-sensing simultaneous localization and radio fingerprint mapping (C-SLAM-RF) in large unknown indoor environments. The proposed system makes use of the received signal strength (RSS) from surrounding Wifi access points (AP) and the motion tracking data from a smart phone (Tango as an example). These measurements are captured duration the walking of multiple users in unknown environments without map information and location of the AP. The experiments were done in a university building with dynamic environment and the results show that the proposed system is capable of estimating the tracks of a group of users with an accuracy of 1.74 meters when compared to the ground truth acquired from a point cloud-based SLAM.