ON THE EFFECTIVENESS OF FEATURE-BASED LIDAR POINT CLOUD REGISTRATION

被引:0
|
作者
Jaw, J. J. [1 ]
Chuang, T. Y. [1 ]
机构
[1] Natl Taiwan Univ, Dept Civil Engn, Taipei 10617, Taiwan
关键词
Feature-based; LIDAR; Registration; 3-D similarity transformation model; LARGE-SCALE; SCENES;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
applications in engineering and management fields. Registration of LIDAR point clouds of consecutive scans or different platforms is a prerequisite for fully exploiting advantages of afore-mentioned applications. In this study, the authors integrate point, line and plane features, commonly seen geometric primitives and readily detected or derived from point clouds, for establishing a multi-feature 3-D similarity transformation model, both functional and stochastic, and illustrate the feasibility of the proposed methodologies on the effectiveness of employed features through theoretical identifications and experimental demonstrations.
引用
收藏
页码:60 / 65
页数:6
相关论文
共 50 条
  • [1] Application of Terrestrial LiDAR for Landslide Monitoring: Lessons Learned from Feature-Based Point Cloud Registration
    Bangaru, Srikanth Sagar
    Shan, Yongwei
    Wang, Chao
    Lewis, Phil
    [J]. CONSTRUCTION RESEARCH CONGRESS 2018: INFRASTRUCTURE AND FACILITY MANAGEMENT, 2018, : 40 - 50
  • [2] AUTOMATIC FEATURE-BASED POINT CLOUD REGISTRATION FOR A MOVING SENSOR PLATFORM
    Weinmann, Martin
    Dittrich, Andre
    Hinz, Stefan
    Jutzi, Boris
    [J]. ISPRS HANNOVER WORKSHOP 2013, 2013, 40-1 (W-1): : 373 - 378
  • [3] A Closed-Form Solution to Planar Feature-Based Registration of LiDAR Point Clouds
    Wang, Yongbo
    Zheng, Nanshan
    Bian, Zhengfu
    [J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2021, 10 (07)
  • [4] A Closed-Form Solution to Linear Feature-Based Registration of LiDAR Point Clouds
    Wang, Yongbo
    Zheng, Nanshan
    Bian, Zhengfu
    Zhang, Hua
    [J]. REMOTE SENSING, 2021, 13 (18)
  • [5] LiDAR Point Cloud Registration based on Improved ICP Method and SIFT Feature
    Zheng, Zhongyang
    Li, Yan
    Jun, Wang
    [J]. PROCEEDINGS OF 2015 IEEE INTERNATIONAL CONFERENCE ON PROGRESS IN INFORMATCS AND COMPUTING (IEEE PIC), 2015, : 588 - 592
  • [6] Point feature-based image registration: A survey
    Xiao, Ming
    Bao, Yong-Liang
    Yan, Zhong-Xing
    [J]. Binggong Xuebao/Acta Armamentarii, 2015, 36 : 326 - 340
  • [7] Feature-based registration between terrestrial and airborne point cloud assisted by topographic maps
    Wu, Hangbin
    Li, Hanyan
    Liu, Chun
    Yao, Lianbi
    [J]. Tongji Daxue Xuebao/Journal of Tongji University, 2015, 43 (03): : 462 - 467
  • [8] FeatSync: 3D point cloud multiview registration with attention feature-based refinement
    Hu, Yiheng
    Li, Binghao
    Xu, Chengpei
    Saydam, Sarp
    Zhang, Wenjie
    [J]. Neurocomputing, 2024, 600
  • [9] Automatic Feature-Based Point Cloud Alignment and Inspection
    Jin, Yu
    Liao, Haitao
    Pierson, Harry
    [J]. 25TH INTERNATIONAL CONFERENCE ON PRODUCTION RESEARCH MANUFACTURING INNOVATION: CYBER PHYSICAL MANUFACTURING, 2019, 39 : 484 - 492
  • [10] Point Cloud Registration Algorithm Based on Extended Point Feature Histogram Feature
    Tang Hui
    Zhou Mingquan
    Geng Guohua
    [J]. LASER & OPTOELECTRONICS PROGRESS, 2019, 56 (24)