27. August 2020 - Providentia editors

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.



1. July 2022

Cognition Factory: Evaluate and visualize camera data

Since the beginning of research on the digital twin, AI specialist Cognition Factory GmbH has focused on processing camera data. In the meantime Dr. Claus Lenz has deployed a large-scale platform


1. July 2022

Digital real-time twin of traffic: ready for series production

Expand the test track, deploy new sensors, decentralize software architecture, fuse sensor data for 24/7 operation of a real-time digital twin, and make data packets public: TU Munich has decisively advanced the Providentia++ research project.


11. May 2022

Elektrobit: Coining Test Lab to stationary data

Elektrobit lays the foundation for Big Data evaluations of traffic data. Simon Tiedemann on the developments in P++.