摘要
为了满足高精度的室内位置服务需求,提出了一种利用K-means聚类改进的迭代最近点(ICP)算法来构建结构化的二维室内地图。通过对二维激光扫描仪获取的点云数据的聚类分析,将每一帧的数据进行聚类,并通过几何中心的平移对点云数据进行预配准,利用聚类及预配准的结果对点云数据进行精确配准得到全局最优解。聚类改进的ICP算法相比于传统的ICP算法,在仅使用单一的二维激光扫描仪采集的点云数据为数据源时,能获得较高精度的配准结果。实验表明,该算法具有适用性强、配准精度高等优点,有助于在单一传感器下快速、精准地构建室内地图。
In order to meet the demand of indoor location service, a new iterative closest point （ICP） algorithm based on K-means clustering is proposed to construct a structured two-dimensional indoor map. Based on the clustering analysis of the point cloud data obtained by the two dimensional laser scanner, the data of each frame is clustered, and the cloud data is pre-registered by the translation of the geometric center. The global optimal solution is obtained by the accuracy registration of the cloud data after clustering and pre-registering. Compared with the traditional ICP algorithm, the improved ICP algorithm can obtain higher accuracy registration results when the point cloud data is collected by a single 2D laser scanner. Experiments show that the algorithm has the advantages of strong robustness and high registration accuracy, which can help to construct the indoor map quickly and accurately under the single sensor.
出处
《激光与光电子学进展》
CSCD
北大核心
2016年第5期196-202,共7页
Laser & Optoelectronics Progress
基金
国家自然科学基金(41371423)
国家863计划(2013AA12A201)
江苏高校优势学科建设工程(SZBF2011-6-B35)
关键词
测量
迭代最近点
K—means聚类
点云配准
室内地图
measurement
iterative closest point
K-means clustering
point cloud registration
indoor map
