用视觉语言模型提升未知室内环境导航效率
Efficient Navigation in Unknown Indoor Environments with Vision-Language Models
- 通过视觉语言模型直接分析不完整地图，零样本推理子目标
- 相比传统方法平均路径缩短10%，减少误入死胡同
- 适合需要高效探索的机器人导航场景
我们提出一种新型高层规划框架，利用视觉语言模型（VLMs）提升在含多个死胡同的未知室内环境中自主导航效率。传统探索方法因全局推理能力有限且依赖局部启发式策略，常走低效路径。相比之下，本方法使VLM能零样本地直接推理占用图，选择更可能带来高效路径的子目标。每个规划步骤中，将3D占用网格转换为部分2D地图并生成候选子目标，由模型评估并排序。该规划方案集成至DYNUS（当前最先进轨迹规划器），在仿真中验证了导航效率提升。VLM从不完整地图中推断结构模式（如房间、走廊），平衡向目标推进与进入未知区域的风险，减少常见贪婪失败（如绕进小房间），平均路径长度缩短约10%。
原文摘要 · Abstract (English)
We present a novel high-level planning framework that leverages vision-language models (VLMs) to improve autonomous navigation in unknown indoor environments with many dead ends. Traditional exploration methods often take inefficient routes due to limited global reasoning and reliance on local heuristics. In contrast, our approach enables a VLM to reason directly about occupancy maps in a zero-shot manner, selecting subgoals that are likely to yield more efficient paths. At each planning step, we convert a 3D occupancy grid into a partial 2D map of the environment, and generate candidate subgoals. Each subgoal is then evaluated and ranked against other candidates by the model. We integrate this planning scheme into DYNUS \cite{kondo2025dynus}, a state-of-the-art trajectory planner, and demonstrate improved navigation efficiency in simulation. The VLM infers structural patterns (e.g., rooms, corridors) from incomplete maps and balances the need to make progress toward a goal against the risk of entering unknown space. This reduces common greedy failures (e.g., detouring into small rooms) and achieves about 10\% shorter paths on average.
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