Problem
Research questions and friction points this paper is trying to address.
UAV
Object Goal Navigation
Large Language Model
Semantic Reasoning
Time Efficiency
Spatial-Semantic Reasoning using Large Language Models for Efficient UAV Search Operations
🤖 AI Summary
该研究通过使用大语言模型实时解析自然语言指令并进行语义推理，结合实时物体检测和3D空间映射技术，提高了无人机目标导航任务的时间效率。
📝 Abstract
We present a real-time semantic navigation framework for Unmanned Aerial Vehicles (UAVs) focused on improving time efficiency in the Object Goal Navigation (ObjectNav) task. Central to our approach is a Large Language Model (LLM) that interprets user-provided natural language instructions and performs semantic reasoning over detected objects and spatial context to prioritize high-probability search regions. The system combines real-time object detection, 3D spatial mapping, and polynomial spline interpolation for smooth and feasible UAV trajectory planning. Unlike prior methods that rely on offline reasoning or simulator-constrained action spaces, our framework can operate in real time, continuously updating semantic relevance based on new observations. Experiments in both simulated and real-world settings demonstrate reductions in mission duration while maintaining high search accuracy, underscoring the effectiveness of LLM-guided reasoning for time- efficient UAV-based ObjectNav.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Large Language Model
Real-time Semantic Navigation
Object Goal Navigation
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