Advances in autonomous driving are inseparable from sensor fusion.
Heterogeneous sensors are widely used for sensor fusion due to their
complementary properties, with radar and camera being the most equipped
sensors. Intrinsic and extrinsic calibration are essential steps in sensor
fusion. The extrinsic calibration, independent of the sensor's own parameters,
and performed after the sensors are installed, greatly determines the accuracy
of sensor fusion. Many target-based methods require cumbersome operating
procedures and well-designed experimental conditions, making them extremely
challenging. To this end, we propose a flexible, easy-to-reproduce and accurate
method for extrinsic calibration of 3D radar and camera. The proposed method
does not require a specially designed calibration environment, and instead
places a single corner reflector (CR) on the ground to iteratively collect
radar and camera data simultaneously using Robot Operating System (ROS), and
obtain radar-camera point correspondences based on their timestamps, and then
use these point correspondences as input to solve the perspective-n-point (PnP)
problem, and finally get the extrinsic calibration matrix. Also, RANSAC is used
for robustness and the Levenberg-Marquardt (LM) nonlinear optimization
algorithm is used for accuracy. Multiple controlled environment experiments as
well as real-world experiments demonstrate the efficiency and accuracy (AED
error is 15.31 pixels and Acc up to 89\%) of the proposed method