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Advances in autonomous driving are inseparable from sensor fusion.Heterogeneous sensors are widely used for sensor fusion due to theircomplementary properties, with radar and camera being the most equippedsensors. Intrinsic and extrinsic calibration are essential steps in sensorfusion. The extrinsic calibration, independent of the sensor's own parameters,and performed after the sensors are installed, greatly determines the accuracyof sensor fusion. Many target-based methods require cumbersome operatingprocedures and well-designed experimental conditions, making them extremelychallenging. To this end, we propose a flexible, easy-to-reproduce and accuratemethod for extrinsic calibration of 3D radar and camera. The proposed methoddoes not require a specially designed calibration environment, and insteadplaces a single corner reflector (CR) on the ground to iteratively collectradar and camera data simultaneously using Robot Operating System (ROS), andobtain radar-camera point correspondences based on their timestamps, and thenuse these point correspondences as input to solve the perspective-n-point (PnP)problem, and finally get the extrinsic calibration matrix. Also, RANSAC is usedfor robustness and the Levenberg-Marquardt (LM) nonlinear optimizationalgorithm is used for accuracy. Multiple controlled environment experiments aswell as real-world experiments demonstrate the efficiency and accuracy (AEDerror is 15.31 pixels and Acc up to 89\%) of the proposed method.

3D Radar and Camera Co-Calibration: A Flexible and Accurate Method for Target-based Extrinsic Calibration