In this paper, we tackle the Aerial Vision-and-Dialog Navigation (AVDN) task in the training-free setting for resource-efficient high-altitude UAV this http URL applying MLLMs leads to unreliable navigation due to weak directional grounding and the lack of explicit spatial this http URL address these issues, we propose PSC-AVDN, a training-free framework that tightly couples a three-stage Parsing-Search-Confirmation reasoning pipeline with a Structured Spatial Memory (SSM).The parsing stage uses an LLM to convert ambiguous dialogue instructions into stable geometric directional and destination cues.A Search Chain-of-Thought (S-CoT) then performs stepwise target exploration under high-altitude observations, and a Confirmation Chain-of-Thought (C-CoT) conducts fine-grained verification around candidate regions to resolve visual this http URL, SSM integrates three complementary sources of spatial cues, including multi-scale visual observation, spatial visual memory, and structured geometric memory to provide global spatial context and long-horizon this http URL experiments on ANDH and ANDH-Full show that PSC-AVDN establishes new state-of-the-art performance in the training-free setting, matching or surpassing several finetuned this http URL will be publicly available at: this https URL
解析、搜索与确认：基于链式推理和结构化空间记忆的无训练空中视觉对话导航 / Parse, Search, and Confirmation: Training-Free Aerial Vision-and-Dialog Navigation with Chain-of-Thought Reasoning and Structured Spatial Memory
本文提出一种无需训练即可让无人机在高层空中通过与人类对话进行导航的新方法，通过将对话指令解析为地理方向线索、利用链式推理逐步搜索目标区域、并经细致确认来消除歧义，同时结合结构化空间记忆全局把握位置信息，从而在资源受限场景下取得匹敌甚至超越传统训练方法的导航性能。
源自 arXiv: 2607.11529
