RANGER：通过视觉上下文适应的单目零样本语义导航框架
RANGER: A Monocular Zero-Shot Semantic Navigation Framework through Visual Contextual Adaptation
AI总结：
RANGER通过视觉上下文学习实现零样本语义导航，无需依赖深度和姿态信息，利用3D基础模型和视觉语言模型提升任务效率与环境适应性。
摘要：
高效的靶标定位和复杂环境中的自主导航是现实世界具身应用的基础。尽管多模态基础模型的进展使零样本物体目标导航成为可能，允许机器人无需微调即可搜索任意物体，现有方法面临两个关键限制：（1）依赖地面真实深度和姿态信息，限制了现实应用场景的适用性；（2）缺乏视觉上下文学习（VICL）能力，无法从环境上下文提取几何和语义先验，如在短行程视频中。为解决这些挑战，我们提出了RANGER，一种新的零样本、开放词汇语义导航框架，仅使用单目相机操作。利用强大的3D基础模型，RANGER消除了对深度和姿态的依赖，同时表现出强大的VICL能力。通过简单观察目标环境的短视频，系统可以在不需架构修改或任务特定再训练的情况下显著提高任务效率。该框架集成了关键帧基于的3D重建、语义点云生成、视觉语言模型（VLM）驱动的探索价值估计、高级自适应航点选择和低级动作执行。在HM3D基准和现实环境中的实验表明，RANGER在导航成功率和探索效率方面表现竞争，同时显示出优越的VICL适应性，无需先前的环境3D映射。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
