基于统计学的小尺寸光点质心快速定位算法

Fast Centroid Location Algorithm of Small Size Light Spot Based on Statistics

在无人机的空中加油和交会对接环节中,为了高速且精确定位无人机外置特征光源点的质心,从而完成对无人机的位姿识别与调控,提出了一种质心定位算法。算法采用改进的Otsu算法对小尺寸光点的图片进行阈值分割,将获得的光源边缘点集随机分段进行小批量椭圆拟合,获得一定数量的质心点,再利用数学统计学方法获得最佳质心点。最后将上述的算法与传统Otsu算法、传统椭圆拟合算法、灰度质心法以及Hough圆检测算法进行实验对比,结果验证上述算法不仅能较稳定地对小尺寸光点图片进行阈值分割,并且在速度和抗干扰能力上都有着显著的优势。

In the aerial rendezvous and docking of UAV,in order to locate the centroid of the external characteristic light source of UAV at high speed and accurately,so as to complete the position and attitude recognition and regulation of UAV,a centroid location algorithm is proposed.Firstly,we improved the Otsu algorithm so that it can perform reasonable threshold segmentation for pictures with small proportion of light point pixels.Then,the obtained light source edge points were randomly segmented and each group of edge points was elliptically fitted to obtain a certain number of centroid points.Then,the best centroid was obtained by mathematical statistics.Finally,the above algorithm was compared with traditional Otsu algorithm,traditional ellipse fitting algorithm,gray centroid algorithm and Hough circle detection algorithm.The result shows that this algorithm can not only segment small light source points stably,but also has obvious advantages in speed and anti-interference ability.