无需训练的无人机导航框架，通过分步推理和空间记忆提升高海拔导航准确率。
Parse, Search, and Confirmation: Training-Free Aerial Vision-and-Dialog Navigation with Chain-of-Thought Reasoning and Structured Spatial Memory
- 三阶段推理流程：解析指令、逐步搜索目标、精细验证候选区域
- 在ANDH和ANDH-Full上达到新最优，超越多个微调方法
- 适合资源受限场景下高海拔无人机自主导航应用
本文针对资源高效高海拔无人机导航中的无训练航空视觉对话导航（AVDN）任务，提出PSC-AVDN框架。传统多模态大模型因方向定位弱、缺乏显式空间记忆导致导航不可靠。PSC-AVDN通过解析-搜索-确认三阶段链式思维推理与结构化空间记忆（SSM）紧密耦合，解决该问题。解析阶段利用大语言模型将模糊对话指令转化为稳定几何方向与目标提示；搜索阶段采用搜索思维链（S-CoT）在高空观测下逐步探索目标；验证阶段使用确认思维链（C-CoT）对候选区域进行细粒度检查以消除视觉歧义。同时，SSM融合多尺度视觉观测、空间视觉记忆与结构化几何记忆，提供全局空间上下文与长时一致性。在ANDH与ANDH-Full数据集上的大量实验表明，PSC-AVDN在无训练设置下达到新最优性能，媲美甚至超越多个微调方法。代码将公开于：https://github.com/QY6616/PSC-AVDN。
原文摘要 · Abstract (English)
In this paper, we tackle the Aerial Vision-and-Dialog Navigation (AVDN) task in the training-free setting for resource-efficient high-altitude UAV navigation.Naively applying MLLMs leads to unreliable navigation due to weak directional grounding and the lack of explicit spatial memory.To 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 ambiguity.Meanwhile, 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 consistency.Extensive 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 methods.Code will be publicly available at: https://github.com/QY6616/PSC-AVDN
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