Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data
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 (orientati...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/8/10/157 |
| _version_ | 1797574139863629824 |
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| author | Ivo Silva Cristiano Pendão Joaquín Torres-Sospedra Adriano Moreira |
| author_facet | Ivo Silva Cristiano Pendão Joaquín Torres-Sospedra Adriano Moreira |
| author_sort | Ivo Silva |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T21:19:41Z |
| format | Article |
| id | doaj.art-67b988788c29495bbbf12042a4924233 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-10T21:19:41Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-67b988788c29495bbbf12042a49242332023-11-19T16:11:32ZengMDPI AGData2306-57292023-10-0181015710.3390/data8100157Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry DataIvo Silva 0Cristiano Pendão1Joaquín Torres-Sospedra2Adriano Moreira3Centro ALGORITMI, Universidade do Minho, Campus de Azurém, 4800-058 Guimarães, PortugalCentro ALGORITMI, Universidade do Minho, Campus de Azurém, 4800-058 Guimarães, PortugalCentro ALGORITMI, Universidade do Minho, Campus de Azurém, 4800-058 Guimarães, PortugalCentro ALGORITMI, Universidade do Minho, Campus de Azurém, 4800-058 Guimarães, PortugalThis 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.https://www.mdpi.com/2306-5729/8/10/157Industry 4.0datasetsfingerprintingmotion sensorsindustrial vehiclesindoor tracking |
| spellingShingle | Ivo Silva Cristiano Pendão Joaquín Torres-Sospedra Adriano Moreira Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data Data Industry 4.0 datasets fingerprinting motion sensors industrial vehicles indoor tracking |
| title | Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data |
| title_full | Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data |
| title_fullStr | Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data |
| title_full_unstemmed | Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data |
| title_short | Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data |
| title_sort | industrial environment multi sensor dataset for vehicle indoor tracking with wi fi inertial and odometry data |
| topic | Industry 4.0 datasets fingerprinting motion sensors industrial vehicles indoor tracking |
| url | https://www.mdpi.com/2306-5729/8/10/157 |
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