University of Oulu

R. Liu et al., "Collaborative SLAM Based on WiFi Fingerprint Similarity and Motion Information," in IEEE Internet of Things Journal, vol. 7, no. 3, pp. 1826-1840, March 2020, doi: 10.1109/JIOT.2019.2957293

Collaborative SLAM based on WiFi fingerprint similarity and motion information

Saved in:
Author: Liu, Ran1,2; Marakkalage, Sumudu Hasala1; Padmal, Madhushanka3;
Organizations: 1Engineering Product Development Pillar, Singapore University of Technology and Design, 8 Somapah Rd, Singapore, 487372
2School of Information Engineering, Southwest University of Science and Technology, 59 Qinglong Road, Mianyang, China, 621010
3Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka, 10400
4School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020060440594
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-06-04
Description:

Abstract

Simultaneous localization and mapping (SLAM) has been extensively 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 widespread deployment of the Wi-Fi wireless network. This article presents a novel approach for collaborative simultaneous localization and radio fingerprint mapping (C-SLAM-RF) in large unknown indoor environments. The proposed system uses received signal strengths (RSS) from Wi-Fi access points (APs) in the existing infrastructure and pedestrian dead reckoning (PDR) from a smartphone, without a prior knowledge about map or distribution of AP in the environment. We claim a loop closure based on the similarity of the two radio fingerprints. To further improve the performance, we incorporate the turning motion and assign a small uncertainty value to a loop closure if a matched turning is identified. The experiment was done in an area of 130 m by 70 m and the results show that our proposed system is capable of estimating the tracks of four users with an accuracy of 0.6 m with Tango-based PDR and 4.76 m with a step counter-based PDR.

see all

Series: IEEE internet of things journal
ISSN: 2327-4662
ISSN-E: 2327-4662
ISSN-L: 2327-4662
Volume: 7
Issue: 3
Pages: 1826 - 1840
DOI: 10.1109/JIOT.2019.2957293
OADOI: https://oadoi.org/10.1109/JIOT.2019.2957293
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work is partly supported by the National Science Foundation of China (No. 61601381, 61750110529, and 61701421) and the Sichuan Science and Technology Program (No. 2019YFH0161 and 2019JDTD0019).
Copyright information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.