基于统计局部特征描述与匹配的点云配准算法

针对ICP算法在初始位姿差、部分数据丢失和噪声干扰情况下鲁棒性差、配准精度低的问题,提出一种基于统计局部特征描述与匹配的点云配准算法。首先,用点云局部密度、点云拟合平面距离方差、高斯曲率和平均曲率构建一个四维的统计局部特征描述符,准确地描述查询点的局部特征;然后,通过点对间的特征差异进行对应点匹配,剔除错误点对,解决点云部分数据缺失和噪声干扰的问题;最后,使用平均匹配距离作为度量改进ICP算法,对点云进行配准,解决初始位姿较差时配准精度低的问题。实验结果表明,该算法在初始位姿差、部分数据丢失和噪声干扰情况下的配准精度提高至少1个量级,配准速率也有较大提升,在鲁棒性和配准精度方面均表现出明显优势。

2D registration based on contour matching for partial matching images

The mean Hausdorff distance, though highly applicable in image registration, does not work well on partial matching images. An improvement upon traditional Hausdorff-distance-based image registration method is proposed, which consists of the following two aspects. One is to estimate transformation parameters between two images from the distributions of geometric property differences instead of establishing explicit feature correspondences. This procedure is treated as the pre-registration. The other aspect is that mean Hausdorff distance computation is replaced with the analysis of the second difference of generalized Hausdorff distance so as to eliminate the redundant points. Experimental results show that our registration method outperforms the method based on mean Hausdorff distance. The registration errors are noticeably reduced in the partial matching images.