针对增强现实中基于目标点云的跟踪与注册问题,提出一种稳健Z分数混合树的配准算法。通过局部邻域内的点至拟合平面的垂直距离以及沿平面法线点的分布来识别噪点,运用绝对中位差增强Z分数的稳健性,同时,采用混合树算法提高最近点的搜索效率。将上述算法应用于增强现实的成像原理中,以对其进行理论论证。分别利用斯坦福大学某研究组的点云数据集和真实采集数据对该算法进行验证。结果表明,在含噪点云集中,该算法能在保持一定精度的同时有效提高配准效率,其用时约为对比算法的5%~10%。

In order to overcome the problems of tracking and registration based on a target point cloud in augmented reality, a robust Z-score hybrid tree registration algorithm is proposed. The noise is identified by the vertical distance from the point in the local neighborhood to the fitting plane and the distribution at normal point of the plane. The robustness of the Z-score is enhanced by utilizing the median absolute deviation; the hybrid tree algorithm is used to improve the efficiency of the nearest-point search. We demonstrate formulation by applying the proposed method to the imaging principle of augmented reality. The proposed algorithm is verified by using the point cloud dataset from a research group in Stanford University and real data. Experimental results show that, for the point cloud dataset with noise, the algorithm can maintain a certain accuracy while effectively improving the registration efficiency, which takes time about 5%-10% of that of the comparison algorithm.