Optical coded targets allow to determine the relative pose of a camera, on a metric scale, from one image only. Furthermore, they are easily and efficiently detected, opening to a wide range of applications in robotics and computer vision. In this work we describe the effect of pixel saturation and non-ideal lens Point Spread Function, causing the apparent position of the corners and the edges of the target to change as a function of the camera exposure time. This effect, which we call exposure bias, is frequent in over- or underexposed images and introduces a systematic error in the estimated camera pose. We propose an algorithm that is able to estimate and correct for the exposure bias exploiting specific geometric features of a common target design based on concentric circles. Through rigorous laboratory experiments carried out in a highly controlled environment, we demonstrate that the proposed algorithm is seven times more precise and three times more accurate in the target distance estimation than the algorithms available in the literature.

光学编码的目标仅允许从一个图像确定公制比例的摄像机相对姿态。此外，可以轻松，有效地检测到它们，从而在机器人技术和计算机视觉中打开了广泛的应用。在这项工作中，我们描述了像素饱和度和非理想的镜头点扩展功能的效果，该效果导致目标的拐角和边缘的视在位置随相机曝光时间而变化。这种效应（我们称为曝光偏差）在曝光过度或曝光不足的图像中很常见，并在估计的相机姿态中引入系统误差。我们提出了一种算法，该算法能够利用同心圆基于共同目标设计的特定几何特征来估计和校正曝光偏差。