机载视景智能辅助驾驶系统为了获取周边态势信息，需要进行大场景、低重叠、高精度的相机标定与危险目标位置测量。针对现有的低重叠度多目相机系统存在标定精度不足，且因畸变导致的视觉测量精度较低等问题，提出了一种自适应Levenberg-Marquardt优化的多相机精确标定方法。首先，建立了基于刚性组合标靶的双目相机标定模型，通过刚性固定的组合标靶和辅助相机，得到双目相机之间的转换关系。然后，提出自适应Levenberg-Marquardt优化算法，以标靶特征点的重投影误差为目标函数，采用自适应步长的迭代优化方法，对双目相机的转换矩阵进行优化，提高了标定精度。最后，完成多相机图像共圆心柱面投影，减少目标视差，提高拼接视景的测量精度。标定实验和测量实验结果表明，提出方法可解决低重叠度视场的相机精确标定与危险目标位置测量的问题，将重投影误差减小10%，测量误差减小13%。

To acquire situational awareness of the surroundings， pilot assistance systems require high-precision camera calibration and hazardous target position measurement in large-scale scenes with low overlap. To address the issues such as insufficient calibration accuracy in existing low-overlap multi-camera systems and reduced visual measurement accuracy due to distortion， this paper proposes a precise multi-camera calibration method based on adaptive Levenberg-Marquardt optimization. First， a binocular camera calibration model is established using a rigid composite target. By employing a rigidly fixed composite target and an auxiliary camera， the transformation relationship between the two cameras is derived. Then， an adaptive Levenberg-Marquardt optimization algorithm is introduced， which uses the reprojection error of the target’s feature points as the objective function. An iterative optimization method with adaptive step sizes is utilized to optimize the transformation matrix between the binocular cameras， thereby enhancing calibration accuracy. Finally， a co-centric cylindrical projection of multi-camera images is performed to reduce target parallax and improve the measurement accuracy of the stitched panoramic view. Calibration and measurement experiments demonstrate that the proposed method effectively addresses the challenges of precise camera calibration and hazardous target position measurement in low-overlapping fields of view， reducing the reprojection error by 10% and the measurement error by 13%.