POINav: 実世界の視覚言語ナビゲーションにおける最終メートル到達のベンチマークと性能向上
POINav: Benchmarking and Enhancing Final-Meters Arrival in Real-World Vision-Language Navigation
実世界のPOI（関心地点）ゴールナビゲーションのための初のクローズドループ評価ベンチマークPOINav-Benchを構築し、POI推論を行うBrainモジュールと連続ウェイポイント予測を行うActionモジュールからなるフレームワークを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Ruiyan Gong, Meisheng Zhang, Yuxiang Zhao, Mingchao Sun, Yanfen Shen, Zedong Chu, Zhining Gu, Wei Guo, Xiaolong Cheng, Qiming Li, Kangning Niu, Yanqing Zhu, Xiaolong Wu, Tianlun Li, Mu Xu
分類: cs.RO, cs.CV
原文アブストラクト
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.
