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
|Author:||Liu, Ran1,2; Marakkalage, Sumudu Hasala1; Padmal, Madhushanka3;|
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
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020060440594
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-06-04
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.
IEEE internet of things journal
|Pages:||1826 - 1840|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
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).
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