Online Camera-to-Ground Targetless Calibration for Advanced Driver Assistance Systems

Online camera-to-ground (C2G) calibration dyna-mically estimates the nonrigid transformation between the camera and the road surface, Traditional static calibration methods are sensitive to variations in tire pressure, vehicle loading volume, and diverse road surfaces. Existing online approaches depend on road elements or photometric consistency between overlapping images, requiring continuous target detection and synchronized cameras for calibration. We propose an online monocular C2G calibration method that operates independently of predefined targets. Our approach employs a coarse-to-fine strategy for ground feature extraction and integrates wheel odometry to refine C2G calibration parameters through nonlinear optimization. To ensure accuracy despite nonrigid transformations during driving, we establish stop criteria to identify reliable calibration parameters and introduce quantitative metrics for performance evaluation. Our algorithm has been validated through both simulation and real-world data experiments, proving its effectiveness and superiority over current state-of-the-art techniques.