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中文摘要：
提出一种适用于机器人导航和环境理解的聚类算法,该算法用来处理各向异性分布的点云数据.算法的基本思想是基于点云的密度分布变化和空间位置分布的不同进行聚类,将信息聚类思想融入传统的DBSCAN算法,既保留了DBSCAN算法抗噪声能力强的优点,又结合点云的空间概率分布改善了聚类结果.算法采用自适应的实时参数估计方法克服全局参数的缺点.在真实环境数据集上的实验证明,所提出的算法可以将点云密度相似但是空间分布不同且互相连接的对象分割开,能处理高噪声点云数据.
英文摘要：
A clustering algorithm for robot navigation and environment understanding is proposed.It is designed to deal with anisotropic distribution point cloud.This algorithm performs clustering according to the variation of density and spatial distribution of points.It combines concepts of information clustering with traditional DBSCAN algorithm.On one hand it keeps antinoise ability,and on the other hand it improves the clustering result by incorporating spatial probability distribution of point cloud.The algorithm uses an adaptive online parameter computing method to conquer the disadvantage of constant global parameter.Experiments on real data set validate that the proposed algorithm can separate connected objects where point cloud has similar density but different spacial distribution,and it can deal with point clouds with high noise.
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