The LuViRA Dataset: Synchronized Vision, Radio, and Audio Sensors for Indoor Localization
(2024) 2024 IEEE International Conference on Robotics and Automation, ICRA 2024- Abstract
- 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... (More)
- 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 (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/9fe9290f-113f-4d49-9f9f-bbd314c2a768
- author
- organization
-
- Integrated Electronic Systems (research group)
- LTH Profile Area: AI and Digitalization
- ELLIIT: the Linköping-Lund initiative on IT and mobile communication
- Communications Engineering (research group)
- Computer Vision and Machine Learning (research group)
- Mathematics (Faculty of Engineering)
- Robotics and Semantic Systems
- LU Profile Area: Natural and Artificial Cognition
- eSSENCE: The e-Science Collaboration
- Lund University Humanities Lab
- LU Profile Area: Nature-based future solutions
- LU Profile Area: Light and Materials
- LU Profile Area: Proactive Ageing
- LTH Profile Area: Engineering Health
- Stroke Imaging Research group (research group)
- Mathematical Imaging Group (research group)
- Department of Electrical and Information Technology
- Embedded Electronics Engineering (M.Sc.)
- publishing date
- 2024-08-08
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2024 IEEE International Conference on Robotics and Automation (ICRA)
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2024 IEEE International Conference on Robotics and Automation, ICRA 2024
- conference location
- Yokohama, Japan
- conference dates
- 2024-05-13 - 2024-05-17
- ISBN
- 979-8-3503-8457-4
- DOI
- 10.1109/ICRA57147.2024.10610237
- language
- English
- LU publication?
- yes
- id
- 9fe9290f-113f-4d49-9f9f-bbd314c2a768
- date added to LUP
- 2024-09-07 07:46:15
- date last changed
- 2024-09-09 11:12:52
@inproceedings{9fe9290f-113f-4d49-9f9f-bbd314c2a768, abstract = {{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}}, author = {{Yaman, Ilayda and Tian, Guoda and Larsson, Martin and Persson, Patrik and Sandra, Michiel and Dürr, Alexander and Tegler, Erik and Challa, Nikhil and Garde, Henrik and Tufvesson, Fredrik and Åström, Kalle and Edfors, Ove and Liu, Liang}}, booktitle = {{2024 IEEE International Conference on Robotics and Automation (ICRA)}}, isbn = {{979-8-3503-8457-4}}, language = {{eng}}, month = {{08}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{The LuViRA Dataset: Synchronized Vision, Radio, and Audio Sensors for Indoor Localization}}, url = {{http://dx.doi.org/10.1109/ICRA57147.2024.10610237}}, doi = {{10.1109/ICRA57147.2024.10610237}}, year = {{2024}}, }