Autonomous unmanned aerial vehicles exploration for semantic indoor reconstruction using 3D Gaussian splatting - Hao Xuan Zhang; Yilin Yang; Zhengbo Zou (Data-Centric Engineering)
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.