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Octree-based Approach for Real-Time 3D Indoor Mapping Using RGB-D Video Data

Hou, Jiwei ; Goebel, Mona ; Hübner, Patrick ; Iwaszczuk, Dorota (2023)
Octree-based Approach for Real-Time 3D Indoor Mapping Using RGB-D Video Data.
12th International Symposium on Mobile Mapping Technology (MMT 2023). Padua, Italy (24.05.2023-26.05.2023)
doi: 10.5194/isprs-archives-XLVIII-1-W1-2023-183-2023
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

3D indoor mapping is becoming increasingly critical for a variety of applications such as path planning and navigation for robots. In recent years, there is a growing interest in how low-cost sensors, such as monocular or depth cameras, can be used for 3D mapping. In our paper, we present an octree-based approach for real-time 3D indoor mapping using a handheld RGB depth camera. One benefit of the generated octree map is that it requires less storage and computational resources than point cloud models. Moreover, it explicitly represents free space and unmapped areas, which are essential for the robot's navigation tasks. In this work, on the basis of the ORB-SLAM3 system (Campos et al., 2021), we developed an octree mapping system, which directly calls the keyframes and estimated poses provided by ORB-SLAM3 algorithms. Furthermore, we used point cloud library (PCL) for the dense point cloud mapping and then OctoMap for the point cloud to octree map conversion. Finally, we implemented an efficient probabilistic 3D mapping in the robot operating system (ROS) environment. We used the TUM RGB-D dataset to evaluate the estimated trajectories of the camera. The evaluation shows an average translational RMSE of 5.9 cm on the TUM RGB-D dataset. Besides, we also compared the ground truth point clouds and our generated point clouds. The result shows the mean cloud-to-cloud distance in the corridor scene is about 6 cm. All the evaluation results show our proposed approach is a promising solution for advanced indoor voxel mapping and robotic navigation systems.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2023
Autor(en): Hou, Jiwei ; Goebel, Mona ; Hübner, Patrick ; Iwaszczuk, Dorota
Art des Eintrags: Bibliographie
Titel: Octree-based Approach for Real-Time 3D Indoor Mapping Using RGB-D Video Data
Sprache: Englisch
Publikationsjahr: 2023
Ort: Padua, Italy
Verlag: Copernicus Publications
Titel der Zeitschrift, Zeitung oder Schriftenreihe: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reihe: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Band einer Reihe: XLVIII-1/W1-2023
Veranstaltungstitel: 12th International Symposium on Mobile Mapping Technology (MMT 2023)
Veranstaltungsort: Padua, Italy
Veranstaltungsdatum: 24.05.2023-26.05.2023
DOI: 10.5194/isprs-archives-XLVIII-1-W1-2023-183-2023
Kurzbeschreibung (Abstract):

3D indoor mapping is becoming increasingly critical for a variety of applications such as path planning and navigation for robots. In recent years, there is a growing interest in how low-cost sensors, such as monocular or depth cameras, can be used for 3D mapping. In our paper, we present an octree-based approach for real-time 3D indoor mapping using a handheld RGB depth camera. One benefit of the generated octree map is that it requires less storage and computational resources than point cloud models. Moreover, it explicitly represents free space and unmapped areas, which are essential for the robot's navigation tasks. In this work, on the basis of the ORB-SLAM3 system (Campos et al., 2021), we developed an octree mapping system, which directly calls the keyframes and estimated poses provided by ORB-SLAM3 algorithms. Furthermore, we used point cloud library (PCL) for the dense point cloud mapping and then OctoMap for the point cloud to octree map conversion. Finally, we implemented an efficient probabilistic 3D mapping in the robot operating system (ROS) environment. We used the TUM RGB-D dataset to evaluate the estimated trajectories of the camera. The evaluation shows an average translational RMSE of 5.9 cm on the TUM RGB-D dataset. Besides, we also compared the ground truth point clouds and our generated point clouds. The result shows the mean cloud-to-cloud distance in the corridor scene is about 6 cm. All the evaluation results show our proposed approach is a promising solution for advanced indoor voxel mapping and robotic navigation systems.

Freie Schlagworte: 3D Indoor Mapping, Real-time, Visual SLAM, Octree, RGB-D Camera
Fachbereich(e)/-gebiet(e): 13 Fachbereich Bau- und Umweltingenieurwissenschaften
13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut für Geodäsie
13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut für Geodäsie > Fernerkundung und Bildanalyse
Hinterlegungsdatum: 30 Mai 2023 05:29
Letzte Änderung: 07 Jun 2023 07:59
PPN: 508367646
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