Targetless Rotational Auto-Calibration of Radar and Camera for Intelligent Transportation Systems
Most intelligent transportation systems use a combination of radar sensors and cameras for robust vehicle perception. The calibration of these heterogeneous sensor types in an automatic fashion during system operation is challenging due to differing physical measurement principles and the high sparsity of traffic radars. We propose – to the best of our knowledge – the first data-driven method for automatic rotational radar-camera calibration without dedicated calibration targets. Our approach is based on a coarse and a fine convolutional neural network. We employ a boosting-inspired training algorithm, where we train the fine network on the residual error of the coarse network. Due to the unavailability of public datasets combining radar and camera measurements, we recorded our own real-world data. We demonstrate that our method is able to reach precise and robust sensor registration and show its generalization capabilities to different sensor alignments and perspectives.
WEITERE AKTUELLE THEMEN
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he more highly automated vehicles are on the road, the more slowly human drivers will drive as well. Drivers need to learn how to deal appropriately with highly automated vehicles.
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In the coming months, the number of sensors in the Providentia++ project will more than triple. To fuse the data from cameras, radars, and lidars, scientists work with probabilities and evaluation pipelines. Questions for Leah Strand from the TU Munich.
Agora Verkehrswende: Does connected driving save energy?
How does connected and automated driving affect vehicles’ net energy consumption? Marena Pützschler, specialist for new mobility and automated driving at Agora Verkehrswende, has studied this question. She tells us about it here.