改进权重的迭代最近点算法在点云配准中的应用

Improved Weight Iterative Closet Point Algorithm Applied in Point Cloud Registration

针对传统迭代最近点算法不具备抗差性的难题,利用迭代最近点算法配准残差的分布规律,综合M估计及选权迭代思想,提出改进权重的迭代最近点配准算法。根据每个点对配准计算出对应的初始权重,然后在附加点对权重的基础上使用选权迭代法计算出满足条件的权重,以达到抵御粗差的目的。结果表明,选权迭代过程能合理改善三维空间转换参数计算的结果,提出的改进算法较适合含粗差点的点云数据的配准。

In this paper we aim to solve the problem that the traditional iterative closet point algorithm is not robust.Using the iterative closet point registration residuals law,the M-estimators and selecting weight iteration,an improved iterative closet point registration algorithm based on the weight of the point cloud is provided.In order to achieve protection against gross errors,using the residuals for each point on the registration calculation to calculate the corresponding initial weight,we use iteration method with variable weights to calculate suitable weight on the basis of additional points of weights.The experimental results indicate that proposed iteration method is capable of improving the effect of registration,and the improved algorithm is suitable for the registration of point cloud with gross errors.