Efficient target localization and autonomous navigation in complex environments are fundamental to real-world embodied applications. While recent advances in multimodal foundation models have enabled zero-shot object goal navigation, allowing robots to search for arbitrary objects without fine-tuning, existing methods face two key limitations: (1) heavy reliance on ground-truth depth and pose information, which restricts applicability in real-world scenarios; and (2) lack of visual in-context learning (VICL) capability to extract geometric and semantic priors from environmental context, as in a short traversal video. To address these challenges, we propose RANGER, a novel zero-shot, open-vocabulary semantic navigation framework that operates using only a monocular camera. Leveraging powerful 3D foundation models, RANGER eliminates the dependency on depth and pose while exhibiting strong VICL capability. By simply observing a short video of the target environment, the system can also significantly improve task efficiency without requiring architectural modifications or task-specific retraining. The framework integrates several key components: keyframe-based 3D reconstruction, semantic point cloud generation, vision-language model (VLM)-driven exploration value estimation, high-level adaptive waypoint selection, and low-level action execution. Experiments on the HM3D benchmark and real-world environments demonstrate that RANGER achieves competitive performance in terms of navigation success rate and exploration efficiency, while showing superior VICL adaptability, with no previous 3D mapping of the environment required.

翻译：在复杂环境中高效定位目标并自主导航是实现现实世界具身智能应用的基础。尽管多模态基础模型的近期进展已实现零样本目标导向导航，使机器人无需微调即可搜索任意物体，但现有方法面临两个关键限制：(1) 严重依赖真实深度与位姿信息，限制了其在真实场景中的适用性；(2) 缺乏视觉上下文学习（VICL）能力，无法从短程遍历视频等环境上下文中提取几何与语义先验。为解决上述挑战，我们提出RANGER——一个仅依赖单目相机的零样本、开放词汇语义导航框架。通过利用强大的3D基础模型，RANGER消除了对深度与位姿的依赖，同时展现出强大的VICL能力。通过简单观察目标环境的短时视频，系统可在无需架构修改或特定任务重新训练的情况下显著提升任务效率。该框架集成了若干关键组件：基于关键帧的三维重建、语义点云生成、视觉-语言模型（VLM）驱动的探索价值估计、高层自适应航点选择以及低层动作执行。在HM3D基准测试与真实环境中的实验表明，RANGER在导航成功率与探索效率方面均表现出竞争力，同时展现出卓越的VICL适应能力，且无需对环境进行预建三维地图。