Semantic-Aware Guided Drone Exploration for Language-Conditioned 3D Indoor Mapping
We present SAGE, a system for open-vocabulary exploration in unknown 3D indoor environments that preserves coverage-oriented behavior while allowing semantic cues to reprioritize frontier selection. In simulations, SAGE outperforms baselines in object discovery and achieves 13.7x speedup over FTU. Real-world drone flights confirm its effectiveness.
Article intelligence
Key points
- SAGE builds on FALCON volumetric explorer integrating CLIP for semantic awareness
- Outperforms FALCON and semantic-only ablation in object discovery on Matterport3D
- 9.0-25.9x faster exploration than FTU (mean 13.7x) with higher volumetric throughput
- Validated on real drone flights; better object discovery than FALCON despite slower exploration
Why it matters
This matters because SAGE builds on FALCON volumetric explorer integrating CLIP for semantic awareness.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.23160] Semantic-Aware Guided Drone Exploration for Language-Conditioned 3D Indoor Mapping
[Submitted on 22 May 2026]
Title:Semantic-Aware Guided Drone Exploration for Language-Conditioned 3D Indoor Mapping
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Abstract:We present Semantic-Aware Guided Exploration, SAGE, a system for open-vocabulary exploration in unknown 3D indoor environments that preserves coverage-oriented behavior while allowing semantic cues to reprioritize frontier selection. Building on the FALCON volumetric explorer, SAGE integrates Contrastive Language-Image Pre-training (CLIP) via four key components: object-centric embedding storage, a temporal cache that projects recent observations onto the free-unknown boundary, object frontiers for high-similarity detections, and a unified semantic-geometric planning cost. This cost function bounds semantic reweighting influence, ensuring frontiers are prioritized without sacrificing total coverage. In Matterport3D-based simulations, SAGE outperforms FALCON and a semantic-only ablation in object discovery across map-query pairs. Compared to Finding Things in the Unknown (FTU), SAGE completes exploration 9.0 to 25.9 times faster across the nine shared map-query pairs, achieving a mean speedup of 13.7. Furthermore, SAGE achieves substantially higher volumetric throughput than FTU. Finally, we deploy SAGE in five real-world flights in two environments on a Modal AI Starling 2 quadrotor with onboard sensing and planning, and offboard CLIP inference. Comparing SAGE and FALCON, we find that while FALCON results in faster exploration and shorter mapping trajectories, SAGE outperforms FALCON in terms of object discovery.
Comments: 10 pages, 6 figures, 4 tables. To be presented at the 2nd 3D-LLM/VLA Workshop at CVPR 2026 (non-archival workshop)
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.23160 [cs.RO]
(or arXiv:2605.23160v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.23160
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Nitin Vegesna [view email] [v1] Fri, 22 May 2026 02:21:58 UTC (4,486 KB)
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