摘要
提出一种基于高斯混合模型与地球移动距离的点集配准算法.将待配准的两个点集均表示为高斯混合模型,其中高斯分布的数量为点集中点的数量,每个高斯分布的均值为点的坐标值,方差为通过优化算法得到的优化值.在配准过程中通过优化两个高斯模型之间的地球移动距离来达到最佳匹配效果.该方法对点集配准中常见的噪声、外点、结构缺失等问题具有较强的鲁棒性.公共数据集与真实车辆平台上的实验表明该算法优于目前流行的点集配准算法.
A point set registration algorithm was presented based on Gaussian mixture model(GMM)and earth mover′s distance(EMD).The two point sets were both denoted by GMM,where the number of Gaussian distributions was equal to the number of points in the point set.The mean of every Gaussian distribution was the locations of points and the covariance was optimized by a heuristic algorithm.The similarity of the two GMM was measured by EMD and optimized by an iterative manner.This method was robust to noise,outliers and missing partial structures.Both experiments on public data sets and real platform validate that the proposed method outperforms some state-of-the-art registration algorithms.
出处
《华中科技大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2017年第10期65-69,共5页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金重大研究计划资助项目(91420101)
关键词
点集配准
高斯混合模型
地球移动距离
机器人定位
点云建图
point set registration
Gaussian mixture model
earth mover's distance
robot localization
point cloud mapping