Registration of Laser Scanning Point Clouds: A Review.
Registration of Laser Scanning Point Clouds: A Review.
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激光扫描点云的注册：回顾
DOI：
10.3390/s18051641
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发表时间：
2018-05-21
期刊：
影响因子：
--
通讯作者：
Chen Y
中科院分区：
文献类型：
--
作者：
Cheng L;Chen S;Liu X;Xu H;Wu Y;Li M;Chen Y
多平台、多角度、多时相激光雷达数据的集成对于地理空间数据应用变得重要。本文综述了激光雷达数据配准在摄影测量和遥感领域的应用。目前，激光雷达点云配准一般采用由粗到精的配准策略。首先使用粗配准方法来实现良好的初始位置，然后基于该初始位置利用细配准方法来细化配准。根据由粗到精的框架，本文回顾了现有的配准方法和方法，并确定了它们之间的重要区别。缺乏标准数据和统一的评价体系被认为是限制不同方法客观比较的一个因素。本文还介绍了最常用的点云配准误差分析方法。最后，讨论了未来激光雷达数据注册的应用程序，数据和技术方面的工作途径。特别是，需要解决来自各种新可用类型的LiDAR硬件的多角度和多尺度数据的注册，这将在森林资源调查，城市能源使用，文化遗产保护和无人驾驶车辆等各种应用中发挥重要作用。
The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.
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DOI：
10.1109/34.121791
发表时间：
1992-02-01
影响因子：
23.6
作者：
BESL, PJ;MCKAY, ND
通讯作者：
MCKAY, ND
影响因子：
1.3
作者：
Al-Durgham, Kaleel;Habib, Ayman
通讯作者：
Habib, Ayman
DOI：
10.1073/pnas.0307513100
发表时间：
2004-04-06
影响因子：
11.1
作者：
Chen, CM
通讯作者：
Chen, CM
DOI：
10.1007/11744023_32
发表时间：
2006-01-01
期刊：
COMPUTER VISION - ECCV 2006 , PT 1, PROCEEDINGS
影响因子：
--
作者：
Bay, Herbert;Tuytelaars, Tinne;Van Gool, Luc
通讯作者：
Van Gool, Luc
DOI：
10.1016/j.isprsjprs.2015.05.006
发表时间：
2015-08-01
影响因子：
12.7
作者：
Abayowa, Bernard O.;Yilmaz, Alper;Hardie, Russell C.
通讯作者：
Hardie, Russell C.