Point Cloud Processing System Development Based on PCL and Qt

Liu Dingning; Ding Qiong

doi:doi:10.11648/j.ijem.20190301.16

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Point Cloud Processing System Development Based on PCL and Qt

Liu Dingning, Ding Qiong

Published in International Journal of Engineering Management (Volume 3, Issue 1)

Received: 10 June 2019 Accepted: 12 July 2019 Published: 31 July 2019

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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.

Published in	International Journal of Engineering Management (Volume 3, Issue 1)
DOI	10.11648/j.ijem.20190301.16
Page(s)	33-39
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Point Cloud Processing, Filtering, Modeling, System Design, PCL

References

[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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Liu Dingning, Ding Qiong. (2019). Point Cloud Processing System Development Based on PCL and Qt. International Journal of Engineering Management, 3(1), 33-39. https://doi.org/10.11648/j.ijem.20190301.16

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ACS Style

Liu Dingning; Ding Qiong. Point Cloud Processing System Development Based on PCL and Qt. Int. J. Eng. Manag. 2019, 3(1), 33-39. doi: 10.11648/j.ijem.20190301.16

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AMA Style

Liu Dingning, Ding Qiong. Point Cloud Processing System Development Based on PCL and Qt. Int J Eng Manag. 2019;3(1):33-39. doi: 10.11648/j.ijem.20190301.16

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@article{10.11648/j.ijem.20190301.16,
  author = {Liu Dingning and Ding Qiong},
  title = {Point Cloud Processing System Development Based on PCL and Qt},
  journal = {International Journal of Engineering Management},
  volume = {3},
  number = {1},
  pages = {33-39},
  doi = {10.11648/j.ijem.20190301.16},
  url = {https://doi.org/10.11648/j.ijem.20190301.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijem.20190301.16},
  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.},
 year = {2019}
}

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TY  - JOUR
T1  - Point Cloud Processing System Development Based on PCL and Qt
AU  - Liu Dingning
AU  - Ding Qiong
Y1  - 2019/07/31
PY  - 2019
N1  - https://doi.org/10.11648/j.ijem.20190301.16
DO  - 10.11648/j.ijem.20190301.16
T2  - International Journal of Engineering Management
JF  - International Journal of Engineering Management
JO  - International Journal of Engineering Management
SP  - 33
EP  - 39
PB  - Science Publishing Group
SN  - 2640-1568
UR  - https://doi.org/10.11648/j.ijem.20190301.16
AB  - 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.
VL  - 3
IS  - 1
ER  -

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Author Information

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

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APA Style

Liu Dingning, Ding Qiong. (2019). Point Cloud Processing System Development Based on PCL and Qt. International Journal of Engineering Management, 3(1), 33-39. https://doi.org/10.11648/j.ijem.20190301.16

Copy | Download

ACS Style

Liu Dingning; Ding Qiong. Point Cloud Processing System Development Based on PCL and Qt. Int. J. Eng. Manag. 2019, 3(1), 33-39. doi: 10.11648/j.ijem.20190301.16

Copy | Download

AMA Style

Liu Dingning, Ding Qiong. Point Cloud Processing System Development Based on PCL and Qt. Int J Eng Manag. 2019;3(1):33-39. doi: 10.11648/j.ijem.20190301.16

Copy | Download

@article{10.11648/j.ijem.20190301.16,
  author = {Liu Dingning and Ding Qiong},
  title = {Point Cloud Processing System Development Based on PCL and Qt},
  journal = {International Journal of Engineering Management},
  volume = {3},
  number = {1},
  pages = {33-39},
  doi = {10.11648/j.ijem.20190301.16},
  url = {https://doi.org/10.11648/j.ijem.20190301.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijem.20190301.16},
  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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Point Cloud Processing System Development Based on PCL and Qt
AU  - Liu Dingning
AU  - Ding Qiong
Y1  - 2019/07/31
PY  - 2019
N1  - https://doi.org/10.11648/j.ijem.20190301.16
DO  - 10.11648/j.ijem.20190301.16
T2  - International Journal of Engineering Management
JF  - International Journal of Engineering Management
JO  - International Journal of Engineering Management
SP  - 33
EP  - 39
PB  - Science Publishing Group
SN  - 2640-1568
UR  - https://doi.org/10.11648/j.ijem.20190301.16
AB  - 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.
VL  - 3
IS  - 1
ER  -

Copy | Download