WSEAS Transactions on Computers
Print ISSN: 1109-2750, E-ISSN: 2224-2872
Volume 23, 2024
Exploring Radar-Camera Extrinsic Calibration: A Deep Learning Perspective and Challenges
Authors: , , ,
Abstract: 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.
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Keywords: sensor fusion, radar-camera calibration, extrinsic calibration, deep learning, autonomous driving, robotics
Pages: 289-299
DOI: 10.37394/23205.2024.23.29