Abstract:

In long-term operation of binocular systems, small drifts in extrinsic parameters may occur due to temperature changes, vibration, and mechanical shocks. As these drifts accumulate, the ranging error increases with distance and seriously degrades long-range measurement performance. Because online compensation is difficult, such small drifts are often neglected. To address this problem, an online fine-tuning method based on position-iteration dual perception is proposed. A multiplicative coupling weighting model is established by integrating spatial reliability with an iteration-stage modulation function. A three-stage progressive optimization algorithm is designed to balance convergence speed and robustness. A simulation framework for offline calibration and online fine-tuning is constructed, together with a fast convergence mechanism for high-confidence initial values. Experiments are conducted on the EuRoC MAV dataset, the proposed method achieves a normalized residual error of 0.601, with the absolute rotation error significantly reduced compared with the offline calibration initialization. The method converges in an average of 18.9 iterations with a runtime of approximately 23.5 ms per frame, demonstrating higher correction accuracy and more stable convergence than the compared methods. Monte Carlo simulations verify the robustness of the algorithm, and system-level simulations demonstrate its feasibility. Cross-scenario validation on the KITTI Odometry dataset further indicates the algorithm's basic generalization ability in real road environments.The proposed method provides an effective modeling and optimization scheme for online fine-tuning of binocular camera extrinsic parameters.

Key words: binocular camera calibration, extrinsic fine-tuning, position-iteration dual perception, progressive optimization, Monte Carlo simulation