USS-Nav: 軽量UAVのゼロショット物体ナビゲーションのための統合時空間意味シーングラフ
USS-Nav: Unified Spatio-Semantic Scene Graph for Lightweight UAV Zero-Shot Object Navigation
未知環境でUAVがゼロショットで物体を探索できるよう、空間接続グラフとオープン語彙の意味情報を統合した階層的シーングラフを逐次構築し、LLMによる大域目標決定と局所プランナを組み合わせた軽量ナビゲーション手法を提案した。
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著者: Weiqi Gai, Yuman Gao, Yuan Zhou, Yufan Xie, Zhiyang Liu, Yuze Wu, Xin Zhou, Fei Gao, Zhijun Meng
分類: cs.RO
原文アブストラクト
Zero-Shot Object Navigation in unknown environments poses significant challenges for Unmanned Aerial Vehicles (UAVs) due to the conflict between high-level semantic reasoning requirements and limited onboard computational resources. To address this, we present USS-Nav, a lightweight framework that incrementally constructs a Unified Spatio-Semantic scene graph and enables efficient Large Language Model (LLM)-augmented Zero-Shot Object Navigation in unknown environments. Specifically, we introduce an incremental Spatial Connectivity Graph generation method utilizing polyhedral expansion to capture global geometric topology, which is dynamically partitioned into semantic regions via graph clustering. Concurrently, open-vocabulary object semantics are instantiated and anchored to this topology to form a hierarchical environmental representation. Leveraging this hierarchical structure, we present a coarse-to-fine exploration strategy: LLM grounded in the scene graph's semantics to determine global target regions, while a local planner optimizes frontier coverage based on information gain. Experimental results demonstrate that our framework outperforms state-of-the-art methods in terms of computational efficiency and real-time update frequency (15 Hz) on a resource-constrained platform. Furthermore, ablation studies confirm the effectiveness of our framework, showing substantial improvements in Success weighted by Path Length (SPL). The source code will be made publicly available to foster further research.
