CaRaCTO: Robust Camera-Radar Extrinsic Calibration with Triple Constraint Optimization

CaRaCTO: Robust Camera-Radar Extrinsic Calibration with Triple Constraint Optimization
Mahdi Chamseddine, Jason Raphael Rambach, Didier Stricker
In: International Conference on Pattern Recognition Applications and Methods. International Conference on Pattern Recognition Applications and Methods (ICPRAM-2024), February 24-26, Rome, Italy, SCITEPRESS, 2024.

Abstract:

The use of cameras and radar sensors is well established in various automation and surveillance tasks. The multimodal nature of the data captured by those two sensors allows for a myriad of applications where one covers for the shortcomings of the other. While cameras can capture high resolution color data, radar can capture the depth and velocity of targets. Calibration is a necessary step before applying fusion algorithms to the data. In this work, a robust extrinsic calibration algorithm is developed for camera-radar setups. The standard geometric constraints used in calibration are extended with elevation constraints to improve the optimization. Furthermore, the method does not rely on any external measurements beyond the camera and radar data, and does not require complex targets unlike existing work. The calibration is done in 3D thus allowing for the estimation of the elevation information that is lost when using 2D radar. The results are evaluated against a sub-millimeter ground truth system and show superior results to existing more complex algorithms. url{https://github.com/mahdichamseddine/CaRaCTO}