计算机科学 ›› 2019, Vol. 46 ›› Issue (7): 274-279.doi: 10.11896/j.issn.1002-137X.2019.07.042
李健1,杨祥如1,何斌2
LI Jian1,YANG Xiang-ru1,HE Bin2
摘要： Kinect等深度相机采集的三维数据往往存在噪音、低分辨率等问题,导致两帧点云的局部几何特征匹配一直面临挑战。目前多采用基于特征直方图的方法解决这一问题,但其计算量较大,且对场景旋转平移的要求较为严格。文中提出了一种基于数据驱动的方法,首先从大量重建好的RGB-D数据集中,通过自监督的深度学习方法构建能够描述三维数据几何特征的模型;然后利用基于KD-Tree的K近邻算法(KNN)得到两部分点云的特征对应点,通过RANSAC剔除误匹配点对;最后通过得到的较准确的位置关系估计两帧点云的几何变换,从而完成配准。基于斯坦福大学点云库中的模型以及真实环境下Kinect采集到的大卫石膏像模型的配准和比较实验表明,所提方法不仅可以提取未知物体的局部几何特征进行配准,还可以较好地应对空间角度变换大的情况。
中图分类号:
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