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

Ran Liu, Sumudu Hasala Marakkalage, Madhushanka Padmal, Thiruketheeswaran Shaganan, Chau Yuen, Yong Liang Guan, U-Xuan Tan

Abstract: Simultaneous localization and mapping (SLAM) has been richly researched in past years particularly with regard to rangebased 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.
Published in: Proceedings of the ION 2019 Pacific PNT Meeting
April 8 - 11, 2019
Hilton Waikiki Beach
Honolulu, Hawaii
Pages: 73 - 83
Cite this article: Liu, Ran, Marakkalage, Sumudu Hasala, Padmal, Madhushanka, Shaganan, Thiruketheeswaran, Yuen, Chau, Guan, Yong Liang, Tan, U-Xuan, "Crowd-sensing Simultaneous Localization and Radio Fingerprint Mapping Based on Probabilistic Similarity Models," Proceedings of the ION 2019 Pacific PNT Meeting, Honolulu, Hawaii, April 2019, pp. 73-83.
https://doi.org/10.33012/2019.16794
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