用语言嵌入的高斯点云实现动态任务导航，兼顾语义规划与避障。
ATLAS Navigator: Active Task-driven LAnguage-embedded Gaussian Splatting
- 构建语言嵌入的分层高斯点云地图，支持在线任务规划
- 在复杂室内外环境中实现60%基准表现，支持实时导航
- 适合需要自然语言指令的机器人自主导航场景
针对非结构化未知环境中的任务导向导航挑战，机器人需实时增量构建并推理具备丰富度量-语义信息的地图。由于任务可能需要澄清或重新定义，地图信息必须足够丰富以支持多种任务泛化。为有效执行自然语言指定的任务，我们提出一种基于语言嵌入高斯点云的分层表示，兼具稀疏语义规划能力以支持在线运行，以及稠密几何表示以实现无碰撞导航。通过在杂乱室内和千米级室外真实环境中的机器人实验验证了该方法的有效性，其性能达到特权基线约60%的水平。更多实验视频与细节见项目页：https://atlasnav.github.io
原文摘要 · Abstract (English)
We address the challenge of task-oriented navigation in unstructured and unknown environments, where robots must incrementally build and reason on rich, metric-semantic maps in real time. Since tasks may require clarification or re-specification, it is necessary for the information in the map to be rich enough to enable generalization across a wide range of tasks. To effectively execute tasks specified in natural language, we propose a hierarchical representation built on language-embedded Gaussian splatting that enables both sparse semantic planning that lends itself to online operation and dense geometric representation for collision-free navigation. We validate the effectiveness of our method through real-world robot experiments conducted in both cluttered indoor and kilometer-scale outdoor environments, with a competitive ratio of about 60% against privileged baselines. Experiment videos and more details can be found on our project page: https://atlasnav.github.io
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