Luděk Žalud

Adam Ligocki, Ales Jelinek, Ludek Zalud

Research on autonomous driving is advancing dramatically and requires new data and techniques to progress even further. To reflect this pressure, we present an extension of our recent work - the Brno Urban Dataset (BUD). The new data focus on winter conditions in various snow-covered environments and feature additional LiDAR and radar sensors for object detection in front of the vehicle. The improvement affects the old data as well. We provide YOLO detection annotations for all old RGB images in the dataset. The detections are further also transferred by our original algorithm into the infra-red (IR) images, captured by the thermal camera. To our best knowledge, it makes this dataset the largest source of machine-annotated thermal images currently available. The dataset is published under MIT license on https://github.com/Robotics-BUT/Brno-Urban-Dataset.

Adam Ligocki, Ales Jelinek, Ludek Zalud

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.