Brno Urban Dataset - The New Data for Self-Driving Agents and Mapping Tasks

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Authors

Ligocki, Adam
Žalud, Luděk
Jelínek, Aleš

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Referee

Mark

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IEEE
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Abstract

Autonomous driving is a dynamically growing field of research, where quality and amount of experimental data is critical. Although several rich datasets are available these days, the demands of researchers and technical possibilities are evolving. Through this paper, we bring a new dataset recorded in Brno, Czech Republic. It offers data from four WUXGA cameras, two 3D LiDARs, inertial measurement unit, infrared camera and especially differential RTK GNSS receiver with centimetre accuracy which, to the best knowledge of the authors, is not available from any other public dataset so far. In addition, all the data are precisely timestamped with sub-millisecond precision to allow wider range of applications. At the time of publishing of this paper, recordings of more than 350 km of rides in varying environment are shared at: https: //github.com/RoboticsBUT/Brno-Urban-Dataset.
Autonomous driving is a dynamically growing field of research, where quality and amount of experimental data is critical. Although several rich datasets are available these days, the demands of researchers and technical possibilities are evolving. Through this paper, we bring a new dataset recorded in Brno, Czech Republic. It offers data from four WUXGA cameras, two 3D LiDARs, inertial measurement unit, infrared camera and especially differential RTK GNSS receiver with centimetre accuracy which, to the best knowledge of the authors, is not available from any other public dataset so far. In addition, all the data are precisely timestamped with sub-millisecond precision to allow wider range of applications. At the time of publishing of this paper, recordings of more than 350 km of rides in varying environment are shared at: https: //github.com/RoboticsBUT/Brno-Urban-Dataset.

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Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA). 2020, p. 3284-3290.
https://ieeexplore.ieee.org/document/9197277

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Peer-reviewed

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en

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