Exploring Radar-Camera Extrinsic Calibration: A Deep Learning Perspective and Challenges

This paper reviews radar-camera extrinsic calibration techniques, focusing on the shift from traditional targetbased methods to advanced deep learning-based targetless approaches. We critically evaluate the benefits and limitations of both methods, highlighting deep learning’s potential for automation and flexibility in dynamic environments. However, challenges such as computational complexity, data requirements, and real-world applicability are also discussed. The paper includes a comparative study of experimental results to provide empirical evidence supporting the theoretical analysis. Future research directions are suggested to address existing challenges and enhance the robustness and efficiency of calibration methods in practical applications.