Acta Univ. Agric. Silvic. Mendelianae Brun. 2013, 61(7), 2415-2421 | DOI: 10.11118/actaun201361072415

Point cloud processing for smart systems

Jaromír Landa, David Procházka, Jiří ©»astný
Department of Informatics, Mendel University in Brno, Zemědělská 1, 613 00, Brno, Czech Republic

High population as well as the economical tension emphasises the necessity of effective city management - from land use planning to urban green maintenance. The management effectiveness is based on precise knowledge of the city environment. Point clouds generated by mobile and terrestrial laser scanners provide precise data about objects in the scanner vicinity. From these data pieces the state of the roads, buildings, trees and other objects important for this decision-making process can be obtained. Generally, they can support the idea of "smart" or at least "smarter" cities.
Unfortunately the point clouds do not provide this type of information automatically. It has to be extracted. This extraction is done by expert personnel or by object recognition software. As the point clouds can represent large areas (streets or even cities), usage of expert personnel to identify the required objects can be very time-consuming, therefore cost ineffective. Object recognition software allows us to detect and identify required objects semi-automatically or automatically.
The first part of the article reviews and analyses the state of current art point cloud object recognition techniques. The following part presents common formats used for point cloud storage and frequently used software tools for point cloud processing. Further, a method for extraction of geospatial information about detected objects is proposed. Therefore, the method can be used not only to recognize the existence and shape of certain objects, but also to retrieve their geospatial properties. These objects can be later directly used in various GIS systems for further analyses.

Keywords: point cloud, object recognition, GIS
Grants and funding:

This work was supported by grant IGA FBE MENDELU 18/2013 (Enhancement of property management effectiveness using point clouds). Data used in this article were provided by GEODIS BRNO, spol. s r. o.

Received: April 11, 2013; Published: December 24, 2013  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Landa, J., Procházka, D., & ©»astný, J. (2013). Point cloud processing for smart systems. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 61(7), 2415-2421. doi: 10.11118/actaun201361072415
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. BIENERT, A., 2007: Tree Detection and Diameter Estimations by Analysis of Forest Terrestrial Laserscanner Point Clouds, ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007, 3: 50-55. Cited from www.dresden-uni.com/die.../2007_Bienert_Helsinki2007.pdf.
    2. EL-HALAWANY, S. I., 2011: Detection of Road Poles from Mobile Terrestrial Laser Scanner Point Cloud. Multi-Platform/Multi-Sensor Remote Sensing and Mapping (M2RSM), 2011 International Workshop. pp. 1-6. Go to original source...
    3. IBRAHIM, S., LICHTI, D., 2012: Curb-Based Street Floor Extraction From Mobile Terrestrial Lidar Point Cloud, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B5, 2012. Cited from http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B5/193/2012/isprsarchives-XXXIX-B5-193-2012.pdf. Go to original source...
    4. LANDA, J., PROCHÁZKA, D., 2012: Usage of Microsoft Kinect for augmented prototyping speed-up, Acta Univ. Agric. et Silvic. Mendel. Brun., 60, 2: 175-180. ISSN 1211-8516. DOI: 10.11118/actaun201260020175 Go to original source...
    5. LLOYD, CH. D., 2010: Local Models for Spatial Analysis, 2. ed., Boca Raton: CRC Press, 352 p. ISBN 978-1-4398-2919-6.
    6. MARTI©EK, D., MARTI©EK, K., PROCHÁZKOVÁ, J., 2007: New methods for space reconstruction of inside cell structures. Journal of Applied Biomedecine, 5, 3: 151-155, ISSN 1214-0287. Go to original source...
    7. MONNIER, F., VALLET, B., SOHEILIAN, B., 2012: Trees Detection from Laser Point Clouds Acquired in Dense Urban Areas by a Mobile Mapping System, XXII ISPRS Congress, Technical Commission III, Boca Raton: CRC Press, I-3, pp. 245-250. ISBN 978-1439829196. Go to original source...
    8. PAULY, M., GROSS, M., KOBBELT, L. P., 2002: Efficient simplification of point-sampled surfaces, Proceedings of the conference on Visualization '02, Washington, DC: IEEE Computer Society, pp. 163-170. ISBN 0-7803-7498-3. Go to original source...
    9. PROCHÁZKOVÁ, J., SEDLÁK, J., PROCHÁZKA, D., 2007: Direct B-Spline Interpolation of CNC Path from Cloud of Points. Proceedings of symposium on computer geometry SCG 2007, Bratislava: Slovenská technická univerzita v Bratislave, pp. 104-110, ISBN 978-80-227-2734-1.
    10. RABBANI, T., VOSSELMAN, C., 2013: Segmentation of Point Clouds Using Smoothness Constraint, ISPRS Commission V Symposium Image Engineering and Vision Metrology. [cit. 2013-2-14]. Cited from http://www.isprs.org/proceedings/XXXVI/part5/paper/RABB_639.pdf.
    11. ROBIN, J. Y., MCLEOD, M., BAART, L., 1998: Geometry and Interpolation of Curves and Surfaces. Cambridge: Cambridge University Press, 430 p. ISBN 9780521159395.
    12. SCHALL, O., BELYAEV, A, SEIDEL, H-P., 2005: Robust Filtering of Noisy Scattered Point Data. Eurographics Symposium on Point-Based Graphics, Aire-la-Ville: Eurographics Association, pp. 71-77. ISBN 3-905673-20-7. Go to original source...
    13. SOTOODEH, S., 2006: Outlier Detection in Laser Scanner Point Clouds. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 35, 5: 297-302. Cited from http://www.isprs.org/proceedings/XXXVI/part5/paper/SOTO_653.pdf.
    14. ©KORPIL, V., ©«ASTNÝ, J., 2008: Comparison of Learning Algorithms. In: 24 the Biennial Symposium on Communications. Kingston, Canada: Queens University Kingston, pp. 231-234. ISBN 978-1-4244-1945-6. Go to original source...
    15. ©«ASTNÝ, J., ©KORPIL, V., 2007: Analysis of Algorithms for Radial Basis Function Neural Network, Personal Wireless Communications, Springer, issue 1, pp. 54-62. ISBN 978-0-387-74158-1. Go to original source...
    16. VOSSELMAN, G., Advanced Point Cloud Processing. [cit. 2013-2-14]. Cited from http://www.ifp.uni-stuttgart.de/publications/phowo09/160Vosselman.pdf.
    17. VOSSELMAN, G., 2010: Hans-Gerd Maas: Airborne and Terrestrial Laser Scanning. 1st ed. Boca Raton: CRC Press, 336 p. ISBN 1439827982.
    18. WEN, X. et al, 2012: Change detection of trees in urban areas using multi-temporal airborne lidar point clouds, Proc. SPIE 8532, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2012. Cited from: http://recherche.ign.fr/labos/matis/pdf/articles_conf/2012/SPIEproceedingWenXiao.pdf.
    19. XIAOHUI, D., 2007: Adaptive Out-of-Core Simplification of Large Point Clouds, Multimedia and Expo, 2007 IEEE International Conference, Beijing: IEEE, pp. 1439-1442. ISBN 1-4244-1016-9.
    20. XIAOJUAN, N. et al, 2009: Segmentation of architecture shape information from 3D point cloud, Proceedings of the 8th International Conference on Virtual Reality Continuum and its Applications in Industry, New York: ACM New York, pp. 127-132. ISBN 978-1-60558-912-1.

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY NC ND 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content