Cooperative Indoor Localization Using Mobile Robot Anchors via Factor Graph Optimization
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| Pubblicato in: | IEEE Internet of Things Journal vol. 12, no. 22 (2025), p. 31420-31431 |
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| Autore principale: | |
| Altri autori: | , , , , , , , , |
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The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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| Accesso online: | Citation/Abstract |
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| 001 | 3246569976 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2327-4662 | ||
| 024 | 7 | |a 10.1109/JIOT.2025.3574883 |2 doi | |
| 035 | |a 3246569976 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 267632 |2 nlm | ||
| 100 | 1 | |a Zhou, Baoding |u Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, the State Key Laboratory of Road Engineering in Extreme Environment, and the Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China | |
| 245 | 1 | |a Cooperative Indoor Localization Using Mobile Robot Anchors via Factor Graph Optimization | |
| 260 | |b The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Reliable indoor localization is crucial for location-based services.Unlike outdoor environments where the Global Navigation Satellite System (GNSS) is prevalent, indoor localization systems employ diverse methods to enhance the accuracy of individual devices. However, these methods face limitations, such as the dependence on pre-existing map data and the necessity of installing anchors. The advancement of the Internet of Things (IoT) and the increasing availability of smart devices have enabled the development of more flexible and dynamic indoor localization solutions. In this article, we propose a novel method to enhance indoor localization through cooperative localization framework. The core concept involves utilizing existing robots as mobile robot anchors to enhance pedestrian localization accuracy through interaction with pedestrians, particularly in environments lacking fixed anchors. We employed a factor graph optimization approach to tightly couple intradevice and interdevice data. This integration dynamically adjusts the inclusion of anchor data based on its quality, thereby minimizing error propagation. The experimental results demonstrate that the localization accuracy of our proposed method better than extend Kalman filter algorithms, emphasizing the potential of mobile IoT devices in indoor localization systems. | |
| 653 | |a Accuracy | ||
| 653 | |a Robots | ||
| 653 | |a Internet of Things | ||
| 653 | |a Localization | ||
| 653 | |a Indoor navigation | ||
| 653 | |a Pedestrians | ||
| 653 | |a Location based services | ||
| 653 | |a Kalman filters | ||
| 653 | |a Optimization | ||
| 653 | |a Global navigation satellite system | ||
| 653 | |a Algorithms | ||
| 700 | 1 | |a Tang, Mengyuan |u College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China | |
| 700 | 1 | |a Liu, Chengjun |u Sinopec Beidou Operation Service Center, and the China-Spacenet Satellite Telecom Company Ltd., Nanjing, China | |
| 700 | 1 | |a Zhong, Xuanke |u College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China | |
| 700 | 1 | |a He, Hao |u College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China | |
| 700 | 1 | |a Chen, Xi |u College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China | |
| 700 | 1 | |a Song, Jiangbo |u College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China | |
| 700 | 1 | |a Wang, Yafei |u College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou, China | |
| 700 | 1 | |a Zhang, Xing |u School of Architecture and Urban Planning, the Guangdong Key Laboratory of Urban Informatics, the MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, and the Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, Shenzhen University, Shenzhen, China | |
| 700 | 1 | |a Li, Qingquan |u Department of Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China | |
| 773 | 0 | |t IEEE Internet of Things Journal |g vol. 12, no. 22 (2025), p. 31420-31431 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Trade & Industry | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3246569976/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |