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Sensor fusion is essential for autonomous driving and autonomous robots, andradar-camera fusion systems have gained popularity due to their complementarysensing capabilities. However, accurate calibration between these two sensorsis crucial to ensure effective fusion and improve overall system performance.Calibration involves intrinsic and extrinsic calibration, with the latter beingparticularly important for achieving accurate sensor fusion. Unfortunately,many target-based calibration methods require complex operating procedures andwell-designed experimental conditions, posing challenges for researchersattempting to reproduce the results. To address this issue, we introduce anovel approach that leverages deep learning to extract a common feature fromraw radar data (i.e., Range-Doppler-Angle data) and camera images. Instead ofexplicitly representing these common features, our method implicitly utilizesthese common features to match identical objects from both data sources.Specifically, the extracted common feature serves as an example to demonstratean online targetless calibration method between the radar and camera systems.The estimation of the extrinsic transformation matrix is achieved through thisfeature-based approach. To enhance the accuracy and robustness of thecalibration, we apply the RANSAC and Levenberg-Marquardt (LM) nonlinearoptimization algorithm for deriving the matrix. Our experiments in the realworld demonstrate the effectiveness and accuracy of our proposed method.

Online Targetless Radar-Camera Extrinsic Calibration Based on the Common Features of Radar and Camera