Dynamic reconstruction in simultaneous localization and mapping based on the segmentation of high variability point zones
Dynamic scene reconstruction in real environments is still an ongoing research challenge; moving objects affect the performance of static environment-based simultaneous localization and mapping and impede a correct scene reconstruction. This paper proposes a method for dynamic scene reconstruction u...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2022.2123062 |
| _version_ | 1798033093246844928 |
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| author | Brayan Andru Montenegro Juan Fernando Flórez Elena Muñoz |
| author_facet | Brayan Andru Montenegro Juan Fernando Flórez Elena Muñoz |
| author_sort | Brayan Andru Montenegro |
| collection | DOAJ |
| description | Dynamic scene reconstruction in real environments is still an ongoing research challenge; moving objects affect the performance of static environment-based simultaneous localization and mapping and impede a correct scene reconstruction. This paper proposes a method for dynamic scene reconstruction using sensor fusion for dynamic simultaneous localization and mapping. It employs two-dimensional LIDAR statistical behaviour to detect and segment high variability point cloud areas containing a dynamic object. The method is computationally low cost, allowing a 6.6 Hz execution rate. It obtains point cloud reconstruction of a static scene by reducing, segmenting, and concatenating successive point clouds of a dynamic environment. The tests were in real indoor environments with a robotic vehicle and a person traversing a scene. The correlation between the static environment point cloud and successive reconstructed point clouds demonstrates that the proposed method reconstructs different environments in the presence of dynamic objects. |
| first_indexed | 2024-04-11T20:23:32Z |
| format | Article |
| id | doaj.art-38325174f0274b649448cfd93fb6e497 |
| institution | Directory Open Access Journal |
| issn | 2164-2583 |
| language | English |
| last_indexed | 2024-04-11T20:23:32Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj.art-38325174f0274b649448cfd93fb6e4972022-12-22T04:04:43ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832022-12-0110176777610.1080/21642583.2022.2123062Dynamic reconstruction in simultaneous localization and mapping based on the segmentation of high variability point zonesBrayan Andru Montenegro0Juan Fernando Flórez1Elena Muñoz2Faculty of Electronic Engineering and Telecommunications, University of Cauca, Popayán, ColombiaFaculty of Electronic Engineering and Telecommunications, University of Cauca, Popayán, ColombiaFaculty of Electronic Engineering and Telecommunications, University of Cauca, Popayán, ColombiaDynamic scene reconstruction in real environments is still an ongoing research challenge; moving objects affect the performance of static environment-based simultaneous localization and mapping and impede a correct scene reconstruction. This paper proposes a method for dynamic scene reconstruction using sensor fusion for dynamic simultaneous localization and mapping. It employs two-dimensional LIDAR statistical behaviour to detect and segment high variability point cloud areas containing a dynamic object. The method is computationally low cost, allowing a 6.6 Hz execution rate. It obtains point cloud reconstruction of a static scene by reducing, segmenting, and concatenating successive point clouds of a dynamic environment. The tests were in real indoor environments with a robotic vehicle and a person traversing a scene. The correlation between the static environment point cloud and successive reconstructed point clouds demonstrates that the proposed method reconstructs different environments in the presence of dynamic objects.https://www.tandfonline.com/doi/10.1080/21642583.2022.2123062Dynamic environmentsensor fusiondynamic SLAMpoint cloud reconstruction |
| spellingShingle | Brayan Andru Montenegro Juan Fernando Flórez Elena Muñoz Dynamic reconstruction in simultaneous localization and mapping based on the segmentation of high variability point zones Systems Science & Control Engineering Dynamic environment sensor fusion dynamic SLAM point cloud reconstruction |
| title | Dynamic reconstruction in simultaneous localization and mapping based on the segmentation of high variability point zones |
| title_full | Dynamic reconstruction in simultaneous localization and mapping based on the segmentation of high variability point zones |
| title_fullStr | Dynamic reconstruction in simultaneous localization and mapping based on the segmentation of high variability point zones |
| title_full_unstemmed | Dynamic reconstruction in simultaneous localization and mapping based on the segmentation of high variability point zones |
| title_short | Dynamic reconstruction in simultaneous localization and mapping based on the segmentation of high variability point zones |
| title_sort | dynamic reconstruction in simultaneous localization and mapping based on the segmentation of high variability point zones |
| topic | Dynamic environment sensor fusion dynamic SLAM point cloud reconstruction |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2022.2123062 |
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