ICON drone: Autonomous indoor exploration using Unmanned Aerial Vehicle for semantic 3D reconstruction
Bibliographic record
Abstract
Keeping an up-to-date 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 drone, an unmanned aerial vehicle (UAV) designed to navigate indoor environments autonomously and generate point clouds. ICON drone is constructed using a 250mm quadcopter frame, a Pixhawk flight controller, an onboard computer, an RGB-D camera and an IMU sensor. The UAV navigates autonomously using a visual-inertial odometer (VIO) and frontier-based exploration. The collected RGB images during the flight are used for 3D reconstruction and semantic segmentation. The final outputs are point clouds with building components and material labelling for BIM generation. We tested the UAV in three scenes in an educational building: classroom, lobby, and lounge. Results show that the ICON drone could: 1) explore all three scenes autonomously, 2) generate absolute scale point clouds with mean point-to-point distances of 0.0644, 0.0518, 0.0727m compared to point clouds collected using a high-fidelity terrestrial LiDAR scanner, 3) label the point cloud with corresponding building components and material with mIoU of 0.588 and 0.629.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".