Autonomous unmanned aerial vehicles exploration for semantic indoor reconstruction using 3D Gaussian splatting
Keeping an up-to-date three-dimensional (3D) representation of buildings is a crucial yet time-consuming step for Building Information Modeling (BIM) and digital twins. To address this issue, we propose ICON (Intelligent CONstruction) drone, an unmanned aerial vehicle (UAV) designed to navigate indo...
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Cambridge University Press
2025-01-01
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| Series: | Data-Centric Engineering |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2632673625100178/type/journal_article |
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| author | Hao Xuan Zhang Yilin Yang Zhengbo Zou |
| author_facet | Hao Xuan Zhang Yilin Yang Zhengbo Zou |
| author_sort | Hao Xuan Zhang |
| collection | DOAJ |
| description | Keeping an up-to-date three-dimensional (3D) representation of buildings is a crucial yet time-consuming step for Building Information Modeling (BIM) and digital twins. To address this issue, we propose ICON (Intelligent CONstruction) drone, an unmanned aerial vehicle (UAV) designed to navigate indoor environments autonomously and generate point clouds. ICON drone is constructed using a 250 mm quadcopter frame, a Pixhawk flight controller, and is equipped with an onboard computer, an Red Green Blue-Depth camera and an IMU (Inertial Measurement Unit) sensor. The UAV navigates autonomously using visual-inertial odometer and frontier-based exploration. The collected RGB images during the flight are used for 3D reconstruction and semantic segmentation. To improve the reconstruction accuracy in weak-texture areas in indoor environments, we propose depth-regularized planar-based Gaussian splatting reconstruction, where we use monocular-depth estimation as extra supervision for weak-texture areas. The final outputs are point clouds with building components and material labels. We tested the UAV in three scenes in an educational building: the classroom, the lobby, and the lounge. Results show that the ICON drone could: (1) explore all three scenes autonomously, (2) generate absolute scale point clouds with F1-score of 0.5806, 0.6638, and 0.8167 compared to point clouds collected using a high-fidelity terrestrial LiDAR scanner, and (3) label the point cloud with corresponding building components and material with mean intersection over union of 0.588 and 0.629. The reconstruction algorithm is further evaluated on ScanNet, and results show that our method outperforms previous methods by a large margin on 3D reconstruction quality. |
| format | Article |
| id | doaj-art-d71b4c361b014d3c8805d71e845d5036 |
| institution | Kabale University |
| issn | 2632-6736 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data-Centric Engineering |
| spelling | doaj-art-d71b4c361b014d3c8805d71e845d50362025-08-20T04:00:48ZengCambridge University PressData-Centric Engineering2632-67362025-01-01610.1017/dce.2025.10017Autonomous unmanned aerial vehicles exploration for semantic indoor reconstruction using 3D Gaussian splattingHao Xuan Zhang0Yilin Yang1Zhengbo Zou2https://orcid.org/0000-0002-7789-655XCivil Engineering, The University of British Columbia, Vancouver, BC, CanadaCivil Engineering, The University of British Columbia, Vancouver, BC, CanadaCivil Engineering and Engineering Mechanics, https://ror.org/00hj8s172 Columbia University , New York, NY, USAKeeping an up-to-date three-dimensional (3D) representation of buildings is a crucial yet time-consuming step for Building Information Modeling (BIM) and digital twins. To address this issue, we propose ICON (Intelligent CONstruction) drone, an unmanned aerial vehicle (UAV) designed to navigate indoor environments autonomously and generate point clouds. ICON drone is constructed using a 250 mm quadcopter frame, a Pixhawk flight controller, and is equipped with an onboard computer, an Red Green Blue-Depth camera and an IMU (Inertial Measurement Unit) sensor. The UAV navigates autonomously using visual-inertial odometer and frontier-based exploration. The collected RGB images during the flight are used for 3D reconstruction and semantic segmentation. To improve the reconstruction accuracy in weak-texture areas in indoor environments, we propose depth-regularized planar-based Gaussian splatting reconstruction, where we use monocular-depth estimation as extra supervision for weak-texture areas. The final outputs are point clouds with building components and material labels. We tested the UAV in three scenes in an educational building: the classroom, the lobby, and the lounge. Results show that the ICON drone could: (1) explore all three scenes autonomously, (2) generate absolute scale point clouds with F1-score of 0.5806, 0.6638, and 0.8167 compared to point clouds collected using a high-fidelity terrestrial LiDAR scanner, and (3) label the point cloud with corresponding building components and material with mean intersection over union of 0.588 and 0.629. The reconstruction algorithm is further evaluated on ScanNet, and results show that our method outperforms previous methods by a large margin on 3D reconstruction quality.https://www.cambridge.org/core/product/identifier/S2632673625100178/type/journal_articleGaussian splattingsscan-to-BIMsemantic segmentationUAV |
| spellingShingle | Hao Xuan Zhang Yilin Yang Zhengbo Zou Autonomous unmanned aerial vehicles exploration for semantic indoor reconstruction using 3D Gaussian splatting Data-Centric Engineering Gaussian splattings scan-to-BIM semantic segmentation UAV |
| title | Autonomous unmanned aerial vehicles exploration for semantic indoor reconstruction using 3D Gaussian splatting |
| title_full | Autonomous unmanned aerial vehicles exploration for semantic indoor reconstruction using 3D Gaussian splatting |
| title_fullStr | Autonomous unmanned aerial vehicles exploration for semantic indoor reconstruction using 3D Gaussian splatting |
| title_full_unstemmed | Autonomous unmanned aerial vehicles exploration for semantic indoor reconstruction using 3D Gaussian splatting |
| title_short | Autonomous unmanned aerial vehicles exploration for semantic indoor reconstruction using 3D Gaussian splatting |
| title_sort | autonomous unmanned aerial vehicles exploration for semantic indoor reconstruction using 3d gaussian splatting |
| topic | Gaussian splattings scan-to-BIM semantic segmentation UAV |
| url | https://www.cambridge.org/core/product/identifier/S2632673625100178/type/journal_article |
| work_keys_str_mv | AT haoxuanzhang autonomousunmannedaerialvehiclesexplorationforsemanticindoorreconstructionusing3dgaussiansplatting AT yilinyang autonomousunmannedaerialvehiclesexplorationforsemanticindoorreconstructionusing3dgaussiansplatting AT zhengbozou autonomousunmannedaerialvehiclesexplorationforsemanticindoorreconstructionusing3dgaussiansplatting |