Towards terrestrial 3D data registration improved by parallel programming and evaluated with geodetic precision
PBN-AR
Instytucja
Wydział Geodezji i Kartografii (Politechnika Warszawska)
Informacje podstawowe
Główny język publikacji
en
Czasopismo
Automation in Construction
ISSN
0926-5805
EISSN
Wydawca
DOI
URL
Rok publikacji
2014
Numer zeszytu
Strony od-do
78-91
Numer tomu
47
Identyfikator DOI
Liczba arkuszy
0.65
Autorzy
Pozostali autorzy
+ 3
Słowa kluczowe
en
Iterative closest point; Data registration; Mobile mapping; CUDA parallel programming; Spatial design support
Streszczenia
Język
en
Treść
In this paper a quantitative and qualitative evaluation of proposed ICP-based data registration algorithm, improved by parallel programming in CUDA (compute unified device architecture), is shown. The algorithm was tested on data collected with a 3D terrestrial laser scanner Z + F Imager 5010 mounted on the mobile platform PIONNER 3AT. Parallel implementation enables data registration on-line, even using a laptop with a standard hardware configuration (graphic card NVIDIA GeForce 6XX/7XX series). Robustness is assured by the use of CUDA-enhanced fast NNS (nearest neighbor search) applied for ICP (iterative closest point) with SVD (singular value decomposition) solver. The evaluation is based on the reference ground truth data registered with geodetic precision. The geodetic approach extends our previous work and gives an accurate benchmark for the algorithm. The data were collected in an urban area under a demolition scenario in a real environment. We compared four registration strategies concerning data preprocessing, such as subsampling and vegetation removal. The result is the analysis of measured performance and the accuracy of the geometric maps. The system provides accurate metric maps on-line and can be used in several applications such as mobile robotics for construction area modelling or spatial design support. It is a core component for our future work on mobile mapping systems.
Inne
System-identifier
WUTc4bc8b4d92d240f39bb2846ee1608302
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