TOF点云强度特征匹配迭代最近点配准算法

Iterative Closest Point registration algorithm based on intensity feature matching of TOF point cloud

摘要:

针对时间飞行(TOF)获取的三维点云数据噪声点多、有效目标在点云中所占比例较小的问题，提出一种适用于TOF点云数据的基于强度特征匹配的迭代最近点配准算法。首先使用强度特征进行有效区域提取，然后对有效区域进行配准，最后使用有效区域的变化矩阵对整个点云数据进行配准。实验结果表明，该方法能在不影响配准速度的情况下，有效提高真实点云配准的精确度。

Abstract:

There are many noise points in the 3D point cloud dala obtained by Time Of Flight (TOF) and the proportion of real objects in the point cloud is small. An iterative closest point registration algorithm based on intensity feature matching is proposed for TOF point cloud data. Firstly, the intensity feature is employed to extract the effective region, and then the effective region is configured. Finally, the change matrix of the effective region is utilized to register the whole point cloud data. Experimental results show that this method can effectively improve the registration accuracy of real target point cloud without affecting the registration speed.