基于邻域曲率的低特征辨识度点云配准方法.

In the process of registering point clouds with low feature recognition, traditional methods based on local feature extraction and matching are usually not accurate, while the accuracy and efficiency of methods based on global feature matching are also hard to guarantee. In response to this problem, this paper proposed an improved local feature matching method. In initial registration, it designed a key point extraction method based on normal vector projection covariance analysis. Then it used fast point feature histogram( FPFH) descriptor to characterize these key points,and defined multiple matching conditions to screen the feature points. Finally, it took the sum of the nearest distance of the corresponding points as the optimization goal for rough matching. In fine registration, this paper used the improved iteration closest point(ICP) algorithm, which took the minimum distance from point to plane as the object of iterative optimization, for accurate registration. Experimental results show that, compared with the other three registration methods, the proposed method can maintain high registration accuracy while reducing registration time. [ABSTRACT FROM AUTHOR]