The LuViRA Dataset: Synchronized Vision, Radio, and Audio Sensors for Indoor Localization
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| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866910423537680384 |
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| author | Yaman, Ilayda Tian, Guoda Larsson, Martin Persson, Patrik Sandra, Michiel Dürr, Alexander Tegler, Erik Challa, Nikhil Garde, Henrik Tufvesson, Fredrik Åström, Kalle Edfors, Ove Malkowsky, Steffen Liu, Liang |
| author_facet | Yaman, Ilayda Tian, Guoda Larsson, Martin Persson, Patrik Sandra, Michiel Dürr, Alexander Tegler, Erik Challa, Nikhil Garde, Henrik Tufvesson, Fredrik Åström, Kalle Edfors, Ove Malkowsky, Steffen Liu, Liang |
| contents | We present a synchronized multisensory dataset for accurate and robust indoor localization: the Lund University Vision, Radio, and Audio (LuViRA) Dataset. The dataset includes color images, corresponding depth maps, inertial measurement unit (IMU) readings, channel response between a 5G massive multiple-input and multiple-output (MIMO) testbed and user equipment, audio recorded by 12 microphones, and accurate six degrees of freedom (6DOF) pose ground truth of 0.5 mm. We synchronize these sensors to ensure that all data is recorded simultaneously. A camera, speaker, and transmit antenna are placed on top of a slowly moving service robot, and 89 trajectories are recorded. Each trajectory includes 20 to 50 seconds of recorded sensor data and ground truth labels. Data from different sensors can be used separately or jointly to perform localization tasks, and data from the motion capture (mocap) system is used to verify the results obtained by the localization algorithms. The main aim of this dataset is to enable research on sensor fusion with the most commonly used sensors for localization tasks. Moreover, the full dataset or some parts of it can also be used for other research areas such as channel estimation, image classification, etc. Our dataset is available at: https://github.com/ilaydayaman/LuViRA_Dataset |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_05309 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The LuViRA Dataset: Synchronized Vision, Radio, and Audio Sensors for Indoor Localization Yaman, Ilayda Tian, Guoda Larsson, Martin Persson, Patrik Sandra, Michiel Dürr, Alexander Tegler, Erik Challa, Nikhil Garde, Henrik Tufvesson, Fredrik Åström, Kalle Edfors, Ove Malkowsky, Steffen Liu, Liang Signal Processing Computer Vision and Pattern Recognition Sound Audio and Speech Processing We present a synchronized multisensory dataset for accurate and robust indoor localization: the Lund University Vision, Radio, and Audio (LuViRA) Dataset. The dataset includes color images, corresponding depth maps, inertial measurement unit (IMU) readings, channel response between a 5G massive multiple-input and multiple-output (MIMO) testbed and user equipment, audio recorded by 12 microphones, and accurate six degrees of freedom (6DOF) pose ground truth of 0.5 mm. We synchronize these sensors to ensure that all data is recorded simultaneously. A camera, speaker, and transmit antenna are placed on top of a slowly moving service robot, and 89 trajectories are recorded. Each trajectory includes 20 to 50 seconds of recorded sensor data and ground truth labels. Data from different sensors can be used separately or jointly to perform localization tasks, and data from the motion capture (mocap) system is used to verify the results obtained by the localization algorithms. The main aim of this dataset is to enable research on sensor fusion with the most commonly used sensors for localization tasks. Moreover, the full dataset or some parts of it can also be used for other research areas such as channel estimation, image classification, etc. Our dataset is available at: https://github.com/ilaydayaman/LuViRA_Dataset |
| title | The LuViRA Dataset: Synchronized Vision, Radio, and Audio Sensors for Indoor Localization |
| topic | Signal Processing Computer Vision and Pattern Recognition Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2302.05309 |