Volume 3, Issue 1, June 2019, Page: 33-39
Point Cloud Processing System Development Based on PCL and Qt
Liu Dingning, School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, China
Ding Qiong, School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, China
Received: Jun. 10, 2019;       Accepted: Jul. 12, 2019;       Published: Jul. 31, 2019
DOI: 10.11648/j.ijem.20190301.16      View  153      Downloads  15
Abstract
LiDAR technology has been widely applied in various disciplines as it can obtain 3D information of targets directly and accurately. However, it is still a challenge to processing LiDAR point clouds efficiently as its huge datasets and complicated processing procedures. Current processing methods need integrate multiple software to complete the whole processing procedures to produce final results which needs lots of time effort and cause low efficiency. By analyzing the theories and methods of LiDAR data processing procedures, this research aims to develop a new point cloud processing software based on PCL and Qt. Firstly, the overall design and modules of the processing system was introduced. The main modules include data management, visualization, filtering, segmentation modeling and auxiliary function. Secondly, to improve system security and maintenance convenience, the system adopts the object-oriented programming method to encapsulate private members and methods of classes, and only open public member variables and methods are available to users. The main classes which were employed in this research were explained. Finally, indoor environments datasets were used to verify the point cloud processing system. The results showed that system has strong interactivity, intuitive display, easy to use and comprehensive features and good results can be derived.
Keywords
Point Cloud Processing, Filtering, Modeling, System Design, PCL
To cite this article
Liu Dingning, Ding Qiong, Point Cloud Processing System Development Based on PCL and Qt, International Journal of Engineering Management. Vol. 3, No. 1, 2019, pp. 33-39. doi: 10.11648/j.ijem.20190301.16
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Zhang X H. Theory and Method of Airborne Lidar Measurement Technology. Wuhan: Wuhan University Press, 2007.
[2]
Cheng X J, Jia D F, Cheng X L. Theory and Technology of Massive Point Cloud Data Processing. Shanghai: Tongji University Press, 2014.
[3]
Vosselman G. Slope based filtering of laser altimetry data. International Archives of Photogrammetry and Remote Sensing, 2000, 33 (63/2): 935-942.
[4]
Zhang K Q, Chen S C, Shyu M L, Yan J H, Zhang C C. A progressive morphological filter for removing nonground measurements from airborne LiDAR data. IEEE Transactions on geoscience and remote sensing, 2003, 41 (4): 872-882.
[5]
Tan Y M, Wang S, Xu B, Zhang J B. An improved progressive morphological filter for UAV-based photogrammetric point clouds in river bank monitoring. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 146: 421-429.
[6]
Sui L C, Yang Y. Filtering of airborne LiDAR point cloud based on car (p, q) model and mathematical morphology . Acta Geodaetica et Cartographica Sinica, 2012, 41 (2): 219-224.
[7]
Zuo Z Q, Zhang Z X, Zhang J Q, et al. A high-quality filtering method with adaptive TIN models for urban LiDAR points based on priori-knowledge. Acta Geodaetica et Cartographica Sinica, 2012, 41 (2): 246-251.
[8]
Lin Y B, Wang C, Cheng J, et al., Line segment extraction for large scale unorganized point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 102: 172-183.
[9]
Raina P, Mudur S, Popa T. Sharpness fields in point clouds using deep learning. Computers & Graphics, 2019, 78: 37-53.
[10]
Zhou Z X, Gong J. Automated residential building detection from airborne LiDAR data with deep neural networks. Advanced Engineering Informatics, 2018, 36: 229-241.
[11]
Du S J, Zhang Y S, Zou Z R, Xu S H, He X, Chen S Y. Automatic building extraction from LiDAR data fusion of point and grid-based features. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130: 294-307.
[12]
Li M L, Rottensteiner F, Heipke C. Modelling of buildings from aerial LiDAR point clouds using TINs and label maps. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 152: 24-33.
[13]
Zhong S K, Zhong Z C, Hua J. Surface reconstruction by parallel and unified particle-based resampling from point clouds. Computer Aided Geometric Design, 2019, 71: 43-62.
[14]
Kang Z Z, Yang J T. A probabilistic graphical model for the classification of mobile LiDAR point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 143: 108-123.
[15]
Nguyen H L, Belton D, Helmholz P. Planar surface detection for sparse and heterogeneous mobile laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 151: 141-161.
[16]
Rusu R B. Semantic 3D object maps for everyday manipulation in human living environments. Garching: Institut für Informatik, Technische Universität München, 2009.
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