Li, Zan; Zhao, Xiaohui; Zhao, Zhongliang; Braun, Torsten (2023). CrowdFusion: Multi-Signal Fusion SLAM Positioning Leveraging Visible Light. IEEE internet of things journal, 10(14), pp. 13065-13076. IEEE 10.1109/JIOT.2023.3260205
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CrowdFusion_Multi-Signal_Fusion_SLAM_Positioning_Leveraging_Visible_Light-2.pdf - Accepted Version Available under License Publisher holds Copyright. Download (1MB) | Preview |
With the fast development of location-based services, an ubiquitous indoor positioning approach with high accuracy and low calibration has become increasingly important. In this work, we target on a crowdsourcing approach with zero calibration effort based on visible light, magnetic field and WiFi to achieve sub-meter accuracy. We propose a CrowdFusion Simultaneous Localization and Mapping (SLAM) comprised of coarse-grained and fine-grained trace merging respectively based on the Iterative Closest Point (ICP) SLAM and GraphSLAM. ICP SLAM is proposed to correct the relative locations and directions of crowdsourcing traces and GraphSLAM is further adopted for fine-grained pose optimization. In CrowdFusion SLAM, visible light is used to accurately detect loop closures and magnetic field to extend the coverage. According to the merged traces, we construct a radio map with visible light and WiFi fingerprints. An enhanced particle filter fusing inertial sensors, visible light, WiFi and floor plan is designed, in which visible light fingerprinting is used to improve the accuracy and increase the resampling/rebooting efficiency. We evaluate CrowdFusion based on comprehensive experiments. The evaluation results show a mean accuracy of 0.67m for the merged traces and 0.77m for positioning, merely replying on crowdsourcing traces without professional calibration.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
08 Faculty of Science > Institute of Computer Science (INF) > Communication and Distributed Systems (CDS) 08 Faculty of Science > Institute of Computer Science (INF) |
UniBE Contributor: |
Braun, Torsten |
Subjects: |
000 Computer science, knowledge & systems 500 Science > 510 Mathematics |
ISSN: |
2327-4662 |
Publisher: |
IEEE |
Funders: |
Organisations 2147483647 not found.; Organisations 62171197 not found. |
Language: |
English |
Submitter: |
Dimitrios Xenakis |
Date Deposited: |
29 Jun 2023 14:23 |
Last Modified: |
28 Aug 2023 01:34 |
Publisher DOI: |
10.1109/JIOT.2023.3260205 |
Uncontrolled Keywords: |
Crowdsourcing; simultaneous localization and mapping (SLAM); visible light |
BORIS DOI: |
10.48350/184235 |
URI: |
https://boris.unibe.ch/id/eprint/184235 |
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