基于ICP算法的内河岸线点云数据配准方法  

Registration method of shoreline point cloud data based on ICP algorithm

作　　者：谢磊[1] 王相龙[1] 王树武[1] 艾学杰 

机构地区：[1]武汉理工大学智能交通系统研究中心,湖北武汉430063

出　　处：《水运工程》2016年第1期146-151,共6页Port & Waterway Engineering

基　　金：交通运输部信息化技术研究项目(2013364548200);交通运输部应用基础研究项目(2013329811220);湖北省自然科学基金创新群体项目(2013CFA007)

摘　　要：激光雷达作为一种扫描精度高、数据信息丰富的新型测绘装备,目前正逐步应用于内河岸线监测领域。由于内河岸线的跨度远大于激光雷达的量程,因此需要让激光雷达在不同地点多次扫描以获取完整的岸线轮廓数据。为了解决这种多站点扫描所带来的多站点云数据配准问题,在传统的ICP(最近迭代点)配准算法的基础上,实现了基于KD-Tree数据结构的ICP配准算法。该算法不仅能够将多站点云数据配准至某个统一的坐标系内,而且有效减少了ICP配准算法的数据搜索范围,提高了配准的准确率。经过多次实地测试可以发现,本算法能够有效减少配准时间,并达到相应的配准精度要求。总体来说,算法具有良好的点云配准效果。As a kind of new survey equipment with high detection accuracy and rich data information, laser radar is gradually applied to the information monitoring field about inland river shoreline. Because the length of shoreline is greater than the measuring range of the laser radar, it is necessary to continue scanning different locations and get the complete shoreline outline. To solve the registration problem of multi-location point cloud data caused by continuous scanning, this paper implements the ICP （ Iterative Closet Point） registration algorithm based on the KD-Tree data structure. The proposed algorithm not only concentrates multi-location of point cloud data on a unified coordinate system, but also reduces effectively the data search scope of ICP registration algorithm and improves the accuracy of registration. The results of field tests show that the algorithm can reduce effectively the registration time and achieve corresponding registration accuracy. On the whole, the algorithm implements good processing results of point cloud registration.

关 键 词：航道要素 激光点云 KD-Tree数据结构 ICP配准算法 

分 类 号：U115[交通运输工程]