We introduce LOG-Nav, an efficient layout-aware object-goal navigation approach designed for complex multi-room indoor environments. By planning hierarchically leveraging a global topologigal map with layout information and local imperative approach with detailed scene representation memory, LOG-Nav achieves both efficient and effective navigation. The process is managed by an LLM-powered agent, ensuring seamless effective planning and navigation, without the need for human interaction, complex rewards, or costly training. Our experimental results on the MP3D benchmark achieves 85\% object navigation success rate (SR) and 79\% success rate weighted by path length (SPL) (over 40\% point improvement in SR and 60\% improvement in SPL compared to exsisting methods). Furthermore, we validate the robustness of our approach through virtual agent and real-world robotic deployment, showcasing its capability in practical scenarios.

翻译：本文提出LOG-Nav，一种面向复杂多房间室内环境的高效布局感知目标导航方法。该方法通过分层规划，利用包含布局信息的全局拓扑地图与基于详细场景表征记忆的局部指令性方法，实现了高效且有效的导航。整个过程由一个基于大语言模型（LLM）的智能体管理，确保了无缝、高效的规划与导航，无需人工干预、复杂奖励机制或昂贵的训练成本。我们在MP3D基准测试上的实验结果表明，该方法达到了85%的目标导航成功率（SR）和79%的路径长度加权成功率（SPL）（与现有方法相比，SR提升超过40个百分点，SPL提升60%）。此外，我们通过虚拟智能体与真实世界机器人部署验证了该方法的鲁棒性，展示了其在实用场景中的能力。