Szczegóły publikacji
Opis bibliograficzny
Hough transform for detection of 3D point cloud rotation / Joanna KOSZYK, Łukasz AMBROZIŃSKI, Piotr Łabędź // W: MMAR 2024 [Dokument elektroniczny] : 2024 28th international conference on Methods and Models in Automation and Robotics : 27–30 August 2024, Międzyzdroje, Poland : technical papers : on line proceedings. — Wersja do Windows. — Dane tekstowe. — Danvers : IEEE, cop. 2024. — (International Conference on Methods and Models in Automation and Robotics ; ISSN 2835-2815). — e-ISBN: 979-8-3503-6233-6. — S. 223–228. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 228, Abstr. — Publikacja dostępna online od: 2024-09-19. --- Abstrakt w: MMAR 2024 : 28th international conference on Methods and Models in Automation and Robotics : 27--30 August 2024, Międzyzdroje, Poland : program - abstracts. --- S. 41
Autorzy (3)
- AGHKoszyk Joanna
- AGHAmbroziński Łukasz
- Łabędź Piotr
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 155084 |
|---|---|
| Data dodania do BaDAP | 2024-09-06 |
| Tekst źródłowy | URL |
| DOI | 10.1109/MMAR62187.2024.10680831 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | International Conference on Methods and Models in Automation and Robotics 2024 |
| Czasopismo/seria | International Conference on Methods and Models in Automation and Robotics |
Abstract
In the field of mobile robotics, point clouds are widely used as an environment representation merged into a map. However, point cloud registration can be challenging when rapid movement occurs. In indoor environments, planelike features such as walls, floors, and ceilings are dominant and can be used to aid cloud merging. These features need to be extracted from point clouds. In this paper, we propose an open-source Hough transform implementation in Python. We have adapted this method, conventionally used for detecting shapes in images, to be applied to 3D point clouds and to detect planar features. To illustrate the point-cloud Hough transform algorithm, we generated synthetic point clouds. Additionally, we collected LiDAR measurements in the corridor. Processing the experimental data proved the algorithm’s ability to handle noise and measurement uncertainties. The study included an evaluation of the Hough transform by making a comparison to the ICP method. The Hough Transform provided more accurate results of corridor width measured on a merged point cloud by 0.06 m.
