Survey of Landmark-based Indoor Positioning Technologies

Jang, Beakcheol; Kim, Hyun-Jung; Kim, Jong wook

doi:10.1016/j.inffus.2022.08.013

Cited by 29 publications

(12 citation statements)

References 60 publications

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“…Wireless devices that operate in the radio spectrum now have the potential to interfere with BLE beacons' signals. It will be necessary to investigate performance over a longer period of time and to include additional users and places in the research [36]. By minimizing signals that could contribute to an incorrect prediction, we were able to anticipate a user's location with greater accuracy.…”

Section: Discussionmentioning

confidence: 99%

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

Maaloul¹,

Lejdel²,

Clementini

et al. 2023

Bulletin EEI

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The adoption of Bluetooth beacon technology demonstrates a broad interest in indoor positioning technology because of its low cost and ease of use. Bluetooth beacons usually have an accuracy of fewer than 4 meters. The use of machine learning (ML) leads to results with greater accuracy compared to using traditional filtering methods. In this paper, we provide indoor localization based on Bluetooth beacons using several different ML techniques. We used ML algorithms to locate customers' devices in shopping malls. The extra-trees classifier and k-neighbors classifier found the device with greater than 90% accuracy. Other algorithms were able to determine the location with less accuracy. The results also showed that Bluetooth technology is a valid solution to find the data used to analyze the spatial-temporal behavior of individuals.

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Section: Discussionmentioning

confidence: 99%

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

Maaloul¹,

Lejdel²,

Clementini

et al. 2023

Bulletin EEI

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“…Other positioning systems rely on algorithms that perform Simultaneous Localisation and Mapping (SLAM) using laser, ultrasounds or images, which do not need an on-purpose infrastructure. However, they become dependent on environment changes, and these are usually complex and costly systems [5]. Lastly, some systems attempt to use existent infrastructure to position, such as Wi-Fi or VLP.…”

Section: State Of the Artmentioning

confidence: 99%

A High-Accuracy, Scalable and Affordable Indoor Positioning System Using Visible Light Positioning for Automated Guided Vehicles

Boixader,

Labella,

Catalan

et al. 2023

Electronics

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Indoor Positioning Systems (IPSs) have multiple applications. For example, they can be used to guide people, to locate items in a warehouse and to support the navigation of Automated Guided Vehicles (AGV). Currently most AGVs use local pre-defined navigation systems, but they lack a global localisation system. Integrating both systems is uncommon due to the inherent challenge in balancing accuracy with coverage. Visible Light Position (VLP) offers accurate and fast localisation, but it encounters scalability limitations. To overcome this, this paper presents a novel Image Sensor-based VLP (IS-VLP) identification method that harnesses existing Light Emitting Diode (LED) lighting infrastructure to substitute both navigation and localisation systems effectively in the whole area. We developed an IPS that achieves six-axis positioning at 90 Hz refresh rate using OpenCV’s solvePnP algorithm and embedded computing. This IPS has been validated in a laboratory environment and successfully deployed in a real factory to position an operative AGV. The system has resulted in accuracies better than 12 cm for 95% of the measurements. This work advances towards positioning VLP as an appealing choice for IPS in industrial environments, offering an inexpensive, scalable, accurate and robust solution.

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“…Sensor-based techniques include sound sensors 4 , optical sensors 5 , and infrared sensors 6 , but they require the installation of numerous sensors and have low accuracy 7 . In contrast, signal-based techniques have become the focus of research due to their advantages in ease of installation and high accuracy 8 . Therefore, various wireless signals, such as Wi-Fi, ZigBee, RFID, BLE, UWB, LoRa, Sigfox, and NFC, are widely used in indoor positioning research.…”

Section: Related Workmentioning

confidence: 99%

Research on indoor positioning method based on LoRa-improved fingerprint localization algorithm

Chen,

Yang,

Hao

et al. 2023

Sci Rep

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Traditional fingerprint localization algorithms need help with low localization accuracy, large data volumes, and device dependence. This paper proposes a LoRa-based improved fingerprint localization algorithm-particle swarm optimization-random forest-fingerprint localization for indoor localization. The first improvement step involves creating a new exceptional fingerprint value (referred to as RSSI-RANGE) by adding the Time of Flight ranging value (referred to as RANGE) to the Received Signal Strength Indication (RSSI) value and weighting them together. The second improvement step involves preprocessing the fingerprint data to eliminate gross errors using Gaussian and median filtering. After noise reduction, the particle swarm optimization algorithm is used to optimize the hyper parameters of the random forest algorithm, and the best RSSI-RANGE value is obtained using the random forest algorithm. The Kriging method is then used for interpolation to establish an offline fingerprint database, and the final online recognition and localization are performed. Experimental results demonstrate that the first improvement step improves localization accuracy by 53–57% in different experimental scenarios, while the second improves localization accuracy by 25–31%. When both steps are combined, the localization accuracy is improved by 58–63%. The effectiveness of this method is demonstrated through experiments.

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Survey of Landmark-based Indoor Positioning Technologies

Cited by 29 publications

References 60 publications

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

A High-Accuracy, Scalable and Affordable Indoor Positioning System Using Visible Light Positioning for Automated Guided Vehicles

Research on indoor positioning method based on LoRa-improved fingerprint localization algorithm

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