VI-SLAM2tag: Low-Effort Labeled Dataset Collection for Fingerprinting-Based Indoor Localization

Laska, Marius; Schulz, Till; Grottke, Jan; Blut, Christoph; Blankenbach, Jörg

doi:10.1109/ipin54987.2022.9918148

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…If the RSS value in ( 3 ) is less than −110 dBm, it is represented by 0 [ 36 ]. Additionally, if the angle is greater than 180 degrees or less than −180 degrees, it will be represented as 180 degrees or −180 degrees, respectively.…”

Section: Description Of the Positioning Algorithmmentioning

confidence: 99%

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm

Cheng

Niu

Zhang

et al. 2022

Sensors

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With the intense deployment of wireless systems and the widespread use of intelligent equipment, the requirement for indoor positioning services is increasing, and Wi-Fi fingerprinting has emerged as the most often used approach to identifying indoor target users. The construction time of the Wi-Fi received signal strength (RSS) fingerprint database is short, but the positioning performance is unstable and susceptible to noise. Meanwhile, to strengthen indoor positioning precision, a fingerprints algorithm based on a convolution neural network (CNN) is often used. However, the number of reference points participating in the location estimation has a great influence on the positioning accuracy. There is no standard for the number of reference points involved in position estimation by traditional methods. For the above problems, the grayscale images corresponding to RSS and angle of arrival are fused into RGB images to improve stability. This paper presents a position estimation method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm, which can select appropriate reference points according to the situation. DBSCAN analyses the CNN output and can choose the number of reference points based on the situation. Finally, the position is approximated using the weighted k-nearest neighbors. The results show that the calculation error of our proposed method is at least 0.1–0.3 m less than that of the traditional method.

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Section: Description Of the Positioning Algorithmmentioning

confidence: 99%

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm

Cheng

Niu

Zhang

et al. 2022

Sensors

View full text Add to dashboard Cite

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Learning Indoor Area Localization: The Trade-Off Between Expressiveness and Reliability

Laska

Blankenbach

2023

Machine Learning for Indoor Localization and Navigation

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UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments

Abdullah

Haris

Aziz

et al. 2023

Data

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Recent studies on indoor positioning using Wi-Fi fingerprinting are motivated by the ubiquity of Wi-Fi networks and their promising positioning accuracy. Machine learning algorithms are commonly leveraged in indoor positioning works. The performance of machine learning based solutions are dependent on the availability, volume, quality, and diversity of related data. Several public datasets have been published in order to foster advancements in Wi-Fi based fingerprinting indoor positioning solutions. These datasets, however, lack dual-band Wi-Fi data within symmetric indoor environments. To fill this gap, this research work presents the UTMInDualSymFi dataset, as a source of dual-band Wi-Fi data, acquired within multiple residential buildings with symmetric deployment of access points. UTMInDualSymFi comprises the recorded dual-band raw data, training and test datasets, radio maps and supporting metadata. Additionally, a statistical radio map construction algorithm is presented. Benchmark performance was evaluated by implementing a machine-learning-based positioning algorithm on the dataset. In general, higher accuracy was observed, on the 5 GHz data scenarios. This systematically collected dataset enables the development and validation of future comprehensive solutions, inclusive of novel preprocessing, radio map construction, and positioning algorithms.

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VI-SLAM2tag: Low-Effort Labeled Dataset Collection for Fingerprinting-Based Indoor Localization

Cited by 3 publications

References 18 publications

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm

Learning Indoor Area Localization: The Trade-Off Between Expressiveness and Reliability

UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments

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