Point cloud unification with optimization algorithm

• Autorzy: Błaszczak-Bąk W. , Sobieraj A.
• Języki publikacji: EN

Abstrakty

EN

Terrestrial laser scanning is a technology that enables to obtain three-dimensional data - an accurate representation of reality. During scanning not only desired objects are measured, but also a lot of additional elements. Therefore, unnecessary data is being removed, what has an impact on efficiency of point cloud processing. It can happen while single point clouds are displayed - user decides what he wants to deleted and does it manually, or by using tools provided in dedicated for point cloud processing softwares. In Leica Geosystems Cyclone - software used here in tests, user can apply tools e.g. for merging or unification of point clouds. Both of them change the separate points clouds into one points cloud, however unification can be executed with reduction - low, medium, high, highest or no reduction at all. It should be noted, that the modeled objects may have complex structure and unification with selected type of reduction can have a very big impact on the result of modeling. In such situation it is desirable to apply different types of reduction. In this article authors propose to apply an optimization algorithm on unified point clouds. Unification conducted by means of Cyclone Leica Geosystems (v.7.3.3) enables to merge point clouds and reduced the number of points. The point elimination is determined mainly by spacing between points. It may leads to loose of important points - representing some essential elements of scanned objects or area. Applying optimization algorithm, especially for complex objects, may help to reduce the number of points without losing the information necessary for proper modeling.

Słowa kluczowe

EN

laser scanning; unification; optimization

• Wydawca: Wydawnictwo Uniwersytetu Warmińsko-Mazurskiego w Olsztynie
• Czasopismo: Technical Sciences / University of Warmia and Mazury in Olsztyn
• Rocznik: 2015
• Tom: nr 18(4)
• Strony: 271--282
• Opis fizyczny: Bibliogr. 17 poz., tab., rys.

Twórcy

autor

Błaszczak-Bąk W.

• Institute of Geodesy, University of Warmia and Mazury in Olsztyn

autor

Sobieraj A.

• Chair of Geodesy, Gdansk University of Technology

Bibliografia

• ALKAN R.H., KARSIDAG G. 2012. Analysis of The Accuracy of Terrestrial Laser Scanning Measurements. FIG Working Week 2012, Rome, Italy, 6-10 May.
• ARMESTO-GONZÁLEZ J., RIVEIRO-RODRÍGUEZ B., GONZÁLEZ-AGUILERA D., TERESA RIVAS-BREA M. 2010. Journal of Archeological Science, 37: 3037-3047.
• ASPERSKI J., DELACOURT C., ALLEMAND P., POTHERAT P., J.M., VARREL E. 2010. Application of a Terrestrial Laser Scanner (TLS) to the Study of the Sechilienne Landslide (Ismre, France). Remote Sens., 2(12): 2785-2802.
• DEMIR N., BAYRAM B., ALKIS Z., HELVACI C., ÇETIN I., VÖGTLE T., RINGLE K., STEINLE E. 2004. Laser Scanning for Terrestrial Photogrammetry, Alternative system Or Combined With Traditional System? ISPRS Archives, XXXV(B5): 193-197. On line: http://www.isprs.org/proceedings/XXXV/ congress/comm5/papers/548.pdf.
• BERNAT M., JANOWSKI A., RZEPA S., SOBIERAJ A., SZULWIC J. 2014. Studies on the Use of Terrestrial Laser Scanning in the Maintenance of Buildings Belonging to the Cultural Heritage. 14th SGEM GeoConference on Informatics, Geoinformatics and Remote Sensing, SGEM2014, June 19-25 Conference Proceedings, 3: 307-318, http://dx.doi.org/10.5593/SGEM2014/B23/S10.039.
• BARNEA S., FILIN S. 2008. Keypoint based autonomous registration of terrestrial laser point-cloud. ISPRS Journal of Photogrammetry Remote Sensing, 63: 19-35.
• BŁASZCZAK W. 2006. Optymalizacja dużych zbiorów wyników pomiaru zasilający bazy danych systemów informacji przestrzennej. Praca doktorska. Uniwersytet warmińsko-Mazurski w Olsztynie.
• BŁASZCZAK W., KAMIŃSKI W. 2007. Optymalizacja dużych zbiorów wyników pomiaru zasilających bazy danych systemów informacji przestrzennej. Przegląd Geodezyjny, 6: 7-9.
• BŁASZCZAK-BĄK W., SOBIERAJ A. 2013. Impact of optimization of ALS point cloud on classification. Technical Sciences, 16(2): 147-164.
• BŁASZCZAK-BĄK W., JANOWSKI A., KAMIŃSKI W., RAPIŃSKI J. 2011. Optimization algorithm and filtration using the adaptive TIN model at the stage of initial processing of the ALS point cloud. Canadian Journal of Remote Sensing, 37(6): 583-589.
• BRENNER C., DOLD C., RIPPERDA N. 2008. Coarse orientation of terrestrial laser scans in urban environments. ISPRS Journal of Photogrammetry & Remote Sensing, 63: 4-18.
• FRYŚKOWSKA A., KĘDZIERSKI M. 2010. Wybrane aspekty integracji danych naziemnego i lotniczego skaningu laserowego. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 21: 97-107.
• FIDERA A., CHAPMAN M.A., HONG J. 2004. Terrestrial Lidar for Industrial Metrology Applications: Modelling, Enhancement and Reconstruction. International Archives of Photogrammetry and Remote Sensing, XXXV(B5): 880-883.
• PILECKI R. 2012. Zastosowanie naziemnego skanera laserowego. Czasopismo Techniczne Politechniki Krakowskiej, 109(9-M): 223-233.
• RABBANI T., DIJKMAN S., HEUVEL F., VOSSELMAN G. 2007. An integrated approach for modelling and global registration of point clouds. ISPRS Journal of Photogrammetry & Remote Sensing, 61: 355-370.
• VOZIKIS G., HARING A., VOZIKIS E., KRAUS K. 2004. Laser Scanning: A New Method for Recording and Documentation in Archeology, In Proceeding of FIG Working Week 2004, Athens, Greece, May 22-27, On line: http://fig.net/pub/athens/papers/wsa1/WSA1-4-Vozikis-et-al.pdf.
• YANG B., ZANG Y. 2014. Automated registration of dense terrestrial laser-scanning point clouds using curves. ISPRS Journal of Photogrammetry & Remote Sensing, 95: 109-121.