POINav: 在真实世界视觉语言导航中基准测试与增强最终米级到达
POINav: Benchmarking and Enhancing Final-Meters Arrival in Real-World Vision-Language Navigation
AI总结：
针对真实世界POI导航的“最后几米”挑战，提出首个闭环评估基准POINav-Bench，并设计脑-动作框架结合70K真实标志-入口数据对，实现高保真度导航。
摘要：
真实世界导航本质上由兴趣点（POI）驱动，然而到达精确的POI仍然是一个关键的“最后几米”挑战。现有的POI目标导航的视觉语言导航（VLN）基准通常由于生成的场景而存在粗粒度或显著的模拟到现实差距。为弥合这一差距，我们提出了POINav-Bench，这是第一个专为真实世界POI目标导航闭环评估设计的基准。它包含使用3D高斯泼溅（3DGS）从真实世界捕获重建的11个商业区域，总面积达126,398平方米，涵盖163个不同的POI。通过可通行性感知标注和参考轨迹，POINav-Bench能够在真实、POI丰富的现实环境中对导航智能体进行高保真评估。在此基础上，我们提出了POINav脑-动作框架，其中脑模块执行基于POI的推理以指导动作模块预测用于真实世界执行的连续航点。我们进一步整理了POINav-Dataset，包含70K个真实世界标志-入口对。实验表明，我们的框架为改进真实世界POI目标导航提供了一条可行路径。
英文摘要：
Real-world navigation is fundamentally driven by Points of Interest (POIs), yet reaching a precise POI remains a critical "final-meters" challenge. Existing Vision-Language Navigation (VLN) benchmarks of POI-goal navigation often suffer from coarse granularity or significant sim-to-real gaps due to generated scene. To bridge this gap, we present POINav-Bench, the first benchmark designed for closed-loop evaluation of real-world POI-goal navigation. It comprises 11 commercial areas reconstructed from real-world captures using 3D Gaussian Splatting (3DGS), covering 126,398 $m^{2}$ in total and spanning 163 distinct POIs. With traversability-aware annotations and reference trajectories, POINav-Bench enables high-fidelity evaluation of navigation agents in realistic, POI-rich real-world environments. Building on this, we propose the POINav Brain-Action Framework where a Brain module performs POI-grounded reasoning to guide an Action module in predicting continuous waypoints for real-world execution. We further curate the POINav-Dataset, containing 70K real-world signage-entrance pairs. Experiments show that our framework provides a viable path toward refining real-world POI-goal navigation.
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。
