一张地图寻找万物：面向零样本多目标导航的实时开放词汇映射
One Map to Find Them All: Real-time Open-Vocabulary Mapping for Zero-shot Multi-Object Navigation
AI总结：
本文提出一种面向零样本多目标导航的实时开放词汇特征地图方法，通过概率-语义更新和不确定性驱动的探索，在仿真和真实机器人上均优于现有最先进方法。
摘要：
在复杂环境中高效搜索物体的能力对于许多真实世界的机器人应用至关重要。开放词汇视觉模型的最新进展催生了语义信息驱动的物体导航方法，使机器人无需预先训练即可搜索任意物体。然而，这些零样本方法在处理连续查询时，每次都将环境视为未知。本文引入了一个新的零样本多目标导航基准，允许机器人利用先前搜索中收集的信息更高效地寻找新物体。为解决该问题，我们构建了一个专为实时物体搜索定制的可重用开放词汇特征地图。我们进一步提出了一种概率-语义地图更新方法，以缓解语义特征提取中的常见错误源，并利用这种语义不确定性进行信息驱动的多目标探索。我们在仿真和真实机器人上对一系列物体导航任务评估了该方法，并在 Jetson Orin AGX 上实时运行。结果表明，该方法在单目标和多目标导航任务上均优于现有的最先进方法。更多视频、代码和多目标导航基准可在 https://finnbsch.github.io/OneMap 获取。
英文摘要：
The capability to efficiently search for objects in complex environments is fundamental for many real-world robot applications. Recent advances in open-vocabulary vision models have resulted in semantically-informed object navigation methods that allow a robot to search for an arbitrary object without prior training. However, these zero-shot methods have so far treated the environment as unknown for each consecutive query. In this paper we introduce a new benchmark for zero-shot multi-object navigation, allowing the robot to leverage information gathered from previous searches to more efficiently find new objects. To address this problem we build a reusable open-vocabulary feature map tailored for real-time object search. We further propose a probabilistic-semantic map update that mitigates common sources of errors in semantic feature extraction and leverage this semantic uncertainty for informed multi-object exploration. We evaluate our method on a set of object navigation tasks in both simulation as well as with a real robot, running in real-time on a Jetson Orin AGX. We demonstrate that it outperforms existing state-of-the-art approaches both on single and multi-object navigation tasks. Additional videos, code and the multi-object navigation benchmark will be available on https://finnbsch.github.io/OneMap.
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。
