Landmark-based navigation (e.g. go to the wooden desk) and relative positional navigation (e.g. move 5 meters forward) are distinct navigation challenges solved very differently in existing robotics navigation methodology. We present a new dataset, OC-VLN, in order to distinctly evaluate grounding object-centric natural language navigation instructions in a method for performing landmark-based navigation. We also propose Natural Language grounded SLAM (NL-SLAM), a method to ground natural language instruction to robot observations and poses. We actively perform NL-SLAM in order to follow object-centric natural language navigation instructions. Our methods leverage pre-trained vision and language foundation models and require no task-specific training. We construct two strong baselines from state-of-the-art methods on related tasks, Object Goal Navigation and Vision Language Navigation, and we show that our approach, NL-SLAM, outperforms these baselines across all our metrics of success on OC-VLN. Finally, we successfully demonstrate the effectiveness of NL-SLAM for performing navigation instruction following in the real world on a Boston Dynamics Spot robot.

本研究解决了现有机器人导航方法中针对地标导航和相对位置导航的不同挑战。我们提出了一种新方法NL-SLAM，将自然语言指令与机器人观察和位姿相结合，利用预训练的视觉和语言模型，无需特定任务训练。研究表明，NL-SLAM在物体中心自然语言导航任务OC-VLN中优于现有基准，展示了其在真实场景中对导航指令的有效执行。

面向物体的自然语言引导SLAM用于物体中心的视觉导航