AirVista-II: 動的シーン意味理解に向けた身体性UAVのエージェントシステム
AirVista-II: An Agentic System for Embodied UAVs Toward Dynamic Scene Semantic Understanding
動的環境でUAVが人間の介入なしにシーンを意味理解・推論できるよう、エージェントによるタスク識別・スケジューリングとマルチモーダル知覚、キーフレーム抽出を統合したエンドツーエンドシステムを提案。ゼロショットで多様なUAV動的シーンにおいて高品質な意味理解を実現した。
著者: Fei Lin, Yonglin Tian, Tengchao Zhang, Jun Huang, Sangtian Guan, Fei-Yue Wang
分類: cs.RO, cs.AI
原文アブストラクト
Unmanned Aerial Vehicles (UAVs) are increasingly important in dynamic environments such as logistics transportation and disaster response. However, current tasks often rely on human operators to monitor aerial videos and make operational decisions. This mode of human-machine collaboration suffers from significant limitations in efficiency and adaptability. In this paper, we present AirVista-II -- an end-to-end agentic system for embodied UAVs, designed to enable general-purpose semantic understanding and reasoning in dynamic scenes. The system integrates agent-based task identification and scheduling, multimodal perception mechanisms, and differentiated keyframe extraction strategies tailored for various temporal scenarios, enabling the efficient capture of critical scene information. Experimental results demonstrate that the proposed system achieves high-quality semantic understanding across diverse UAV-based dynamic scenarios under a zero-shot setting.
