Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data

Ivo Silva (), Cristiano Pendão (), Joaquín Torres-Sospedra and Adriano Moreira
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Ivo Silva: Centro ALGORITMI, Universidade do Minho, Campus de Azurém, 4800-058 Guimarães, Portugal
Cristiano Pendão: Centro ALGORITMI, Universidade do Minho, Campus de Azurém, 4800-058 Guimarães, Portugal
Joaquín Torres-Sospedra: Centro ALGORITMI, Universidade do Minho, Campus de Azurém, 4800-058 Guimarães, Portugal
Adriano Moreira: Centro ALGORITMI, Universidade do Minho, Campus de Azurém, 4800-058 Guimarães, Portugal

Data, 2023, vol. 8, issue 10, 1-20

Abstract: This paper describes a dataset collected in an industrial setting using a mobile unit resembling an industrial vehicle equipped with several sensors. Wi-Fi interfaces collect signals from available Access Points (APs), while motion sensors collect data regarding the mobile unit’s movement (orientation and displacement). The distinctive features of this dataset include synchronous data collection from multiple sensors, such as Wi-Fi data acquired from multiple interfaces (including a radio map), orientation provided by two low-cost Inertial Measurement Unit (IMU) sensors, and displacement (travelled distance) measured by an absolute encoder attached to the mobile unit’s wheel. Accurate ground-truth information was determined using a computer vision approach that recorded timestamps as the mobile unit passed through reference locations. We assessed the quality of the proposed dataset by applying baseline methods for dead reckoning and Wi-Fi fingerprinting. The average positioning error for simple dead reckoning, without using any other absolute positioning technique, is 8.25 m and 11.66 m for IMU1 and IMU2, respectively. The average positioning error for simple Wi-Fi fingerprinting is 2.19 m when combining the RSSI information from five Wi-Fi interfaces. This dataset contributes to the fields of Industry 4.0 and mobile sensing, providing researchers with a resource to develop, test, and evaluate indoor tracking solutions for industrial vehicles.

Keywords: Industry 4.0; datasets; fingerprinting; motion sensors; industrial vehicles; indoor tracking; indoor positioning; Wi-Fi; IMU; encoder (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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