This paper deals with the task of autocalibration of scanning electron microscope (SEM), which is a technique allowing to compute camera motion and intrinsic parameters. In contrast to classical calibration, which implies the use of a calibration object and is known to be a tedious and rigid operation, auto- or selfcalibration is performed directly on the images acquired for the visual task. As autocalibration represents an optimization problem, all the steps contributing to the success of the algorithm are presented: formulation of the cost function incorporating metric constraints, definition of bounds, regularization, and optimization algorithm. The presented method allows full estimation of camera matrices for all views in the sequence. It was validated on virtual images as well as on real SEM images (pollen grains, cutting tools, etc.). The results show a good convergence range and low execution time, notably compared to classical methods, and even more in the context of the calibration of SEM.

本文处理扫描电子显微镜（SEM）的自动校准任务，这是一种允许计算相机运动和固有参数的技术。与经典校准相反，经典校准意味着使用校准对象，并且众所周知这是乏味且僵化的操作，而自动校准或自校准直接在为视觉任务获取的图像上执行。由于自动校准代表一个优化问题，因此介绍了有助于算法成功的所有步骤：制定包含度量约束，边界定义，正则化和优化算法的成本函数。提出的方法允许针对序列中的所有视图完全估计相机矩阵。它已在虚拟图像和真实SEM图像（花粉粒，切割工具等）上进行了验证。