言語条件付き3D屋内マッピングのための意味認識型誘導ドローン探索
Semantic-Aware Guided Drone Exploration for Language-Conditioned 3D Indoor Mapping
未知の3D屋内環境を探索するドローンに対し、CLIPによる意味的手がかりで探索優先度を調整しつつ、カバレッジを維持するシステムSAGEを提案した。
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著者: Nitin Vegesna, Avideh Zakhor
分類: cs.RO, cs.CV
原文アブストラクト
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.
関連論文
- 小型自律飛行ロボットによる屋内環境の効率的探索学習探索/ドローン
