离群值
算法
点云
计算机科学
高斯分布
迭代法
转化(遗传学)
点(几何)
数学
人工智能
几何学
生物化学
量子力学
基因
物理
化学
作者
Guangrun Xu,Jianmin Huang,Yueni Lu
摘要
Coherent Point Drift (CPD) is one of the popular robust point cloud registration algorithms in recent years. However, the algorithm uses fast Gaussian transformation to calculate the matrix-vector product, resulting in slower overall registration efficiency. We propose an improved coherent point drift (ICPD) algorithm, which introduces faster Gaussian lattice filtering to calculate the above product and uses the global squared iterative method to reduce the number of iterations of the CPD algorithm. In addition, the outlier w is not accurately expressed in CPD. We propose an iterative outlier formula to solve this problem. Experiments show that the improved algorithm is about two orders of magnitude faster than the CPD algorithm, 1-2 times faster than the ICP algorithm, and shows superior performance in environments with different noise and outlier distributions.
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