基于KFCM的三维点云精简算法

毫米波雷达、TOF相机等扫描设备捕获的点云包含大量的冗余信息与噪声点，降低了点云配准的精度与处理效率。针对上述问题，提出基于KFCM的三维点云精简方法，将原始扫描点云以核函数映射至特征空间，同时更新聚类中心及隶属度矩阵，利用加权误差平方和建立目标函数且以其收敛为算法终止标准，最终将聚类中心作为精简结果输出。实验结果表明，本方法可在良好保持点云基本特征的前提下降低源点云分辨率以达到精简效果，且可通过调节参数控制输出点云的疏密程度，在与配准任务的适应性实验中可满足ICP等配准算法的需求。

3D Point Cloud Simplification Algorithm Based on KFCM

The point cloud captured by millimeter wave radar, TOF camera, and other scanning equipment contains a large number of redundant information and noise points, which reduces the accuracy and processing efficiency of the point cloud registration step. In view of this phenomenon, a 3D point cloud simplification method based on KFCM is proposed. The algorithm maps the original scanned point cloud to feature space by the kernel function. At the same time, the clustering center and membership matrix were updated, and the objective function was established by using the sum of weighted error squares as the convergence mark of the algorithm. Finally, the clustering center was output as the simplified result. Experimental results show that the proposed method can reduce the resolution of source point cloud to achieve simplification effect while maintaining the basic characteristics of point cloud well, and the density of output point cloud can be controlled by adjusting parameters, which can meet the requirements of ICP and other registration algorithms in the adaptability experiment of registration task.