Szczegóły publikacji
Opis bibliograficzny
Methods analysis for the processing of point clouds from terrestrial laser scanning in order to reduce the amount of data / Wojciech MATWIJ // W: SGEM 2017 : 17th international multidisciplinary scientific geoconference : informatics, geoinformatics and remote sensing : 29 June–5 July, 2017, Albena, Bulgaria : conference proceedings. Vol. 17 iss. 21, Informatics, geoinformatics. — Sofia : STEF92 Technology Ltd., cop. 2017. — (International Multidisciplinary Scientific GeoConference SGEM ; ISSN 1314-2704). — ISBN: 978-619-7408-01-0. — s. 991–998. — Bibliogr. s. 997–998, Abstr.
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 107228 |
|---|---|
| Data dodania do BaDAP | 2017-07-20 |
| DOI | 10.5593/SGEM2017/21/S08.125 |
| Rok publikacji | 2017 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | 17th international multidisciplinary scientific geoconference |
| Czasopismo/seria | International Multidisciplinary Scientific GeoConference SGEM |
Abstract
Currently, terrestrial laser scanning is one of the most common method of measurement used for registration of the shape of objects. This technology enables in short time to designate huge collections of spatial data, known as point clouds. Due to the amount of occupied memory, storing large data sets becomes now a big problem. One solution is to create algorithms that allows to reduce the amount of recorded data without losing the essential spatial information. On the market there are mostly paid programs, that allows to perform this type of action, however, the results of their work does not meet users requirements. It is therefore necessary to create own solutions, which can speed up such operations and improve their performance. The article presents various solutions of processing point clouds used in several programs existing on the market. The comparison of methods due to the type of solution was also described. The speed of the algorithm, method of data processing and the quality of results were the subjects of analysis. Conclusions from the analysis allow to determine the drawbacks of existing algorithms and the needs of their continuous development. For this reason, own algorithms used to unify point clouds based on a different approach to the problem have also been created. Due to the simplicity of processing this type of data a MATLAB programming environment was selected. Created solutions and gained experience in the future can help in creation of more advanced algorithms and help to improve processing spatial data.
