Collaborative SLAM based on Wifi Fingerprint Similarity and Motion Information

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

doi:10.48550/arxiv.2001.02759

Cited by 2 publications

(3 citation statements)

References 61 publications

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“…The SLAM approach described in [19] explores smartphones to collaboratively localise and construct the RM of the building. The system merges tracks from different users, performs loop closure detection (when users pass by a previously visited location), and optimizes a graph to generate accurate RM.…”

Section: Related Workmentioning

confidence: 99%

Real-World Deployment of Low-Cost Indoor Positioning Systems for Industrial Applications

2022

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The deployment of an Indoor Position System (IPS) in the real-world raised many challenges, such as installation of infrastructure, the calibration process or modelling of the building's floor plan. For Wi-Fi-based IPSs, deployments often require a laborious and time-consuming site survey to build a Radio Map (RM), which tends to become outdated over time due to several factors. In this paper, we evaluate different deployment methods of a Wi-Fi-based IPS in an industrial environment. The proposed solution works in scenarios with different space restrictions and automatically builds a RM using industrial vehicles in operation. Localization and tracking of industrial vehicles, equipped with low-cost sensors, is achieved with a particle filter, which combines Wi-Fi measurements with heading and displacement data. This allows to automatically annotate and add new samples to a RM, named vehicle Radio Map (vRM), without human intervention. In industrial environments, vRMs can be used with Wi-Fi fingerprinting to locate human operators, industrial vehicles, or other assets, allowing to improve logistics, monitoring of operations, and safety of operators. Experiments in an industrial building show that the proposed solution is capable of automatically building a high-quality vRM in different scenarios, i.e., considering a complete floor plan, a partial floor plan or without a floor plan. Obtained results revealed that vRMs can be used in Wi-Fi fingerprinting with better accuracy than a traditional RM. Sub-meter accuracies were obtained for an industrial vehicle prototype after deployment in a real building.

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Section: Related Workmentioning

confidence: 99%

Real-World Deployment of Low-Cost Indoor Positioning Systems for Industrial Applications

2022

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“…In an effort to avoid repeating the site survey, which is laborious and time-consuming, some solutions explore fixed anchors collecting FPs to refresh the RM [10,11,13], while others [9,14] explore crowdsourced data from users to perform automatic construction of RMs. SLAM has also been explored to perform the site survey [8,15]. In addition, there are techniques to optimize the RM [3] hence, avoiding the RM maintenance process.…”

Section: Related Workmentioning

confidence: 99%

“…RM maintenance can be accomplished by performing periodic manual site surveys, or by exploring methods for automatic maintenance, e.g., using Simultaneous Localization and Mapping (SLAM) [8] or collaborative approaches [9] where users contribute to the RM, among others [10,11]. Despite those ways of updating the RM (manually or automatically), selecting the appropriate timing for the update is still an open issue.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting

Silva

Pendão

Torres-Sospedra³

et al. 2021

2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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One of the most common assumptions regarding indoor positioning systems based on Wi-Fi fingerprinting is that the Radio Map (RM) becomes outdated and has to be updated to maintain the positioning performance. It is known that propagation effects, the addition/removal of Access Points (APs), changes in the indoor layout, among others, cause RMs to become outdated. However, there is a lack of studies that show how the RM degrades over time. In this paper, we describe an empirical study, based on real-world experiments, to evaluate how and why RMs degrade over time. We conducted site surveys and deployed monitoring devices to analyse the radio environment of one building over 2+ years, which allowed us to identify significant changes/events that caused the degradation of RMs. To quantify the RM degradation, we use the positioning error and propose the RM degradation ratio, a metric to directly compare two RMs and measure how different they are. Obtained results show that the positioning performance is much better when RMs are collected on the same day as the test data, and although RM degradation tends to increase over time, it only leads to large positioning errors when significant changes occur in the Wi-Fi infrastructure, making previous RMs outdated.

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Collaborative SLAM based on Wifi Fingerprint Similarity and Motion Information

Cited by 2 publications

References 61 publications

Real-World Deployment of Low-Cost Indoor Positioning Systems for Industrial Applications

Real-World Deployment of Low-Cost Indoor Positioning Systems for Industrial Applications

Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting

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