WildOS: 野外环境中的开放词汇物体搜索
WildOS: Open-Vocabulary Object Search in the Wild
AI总结：
WildOS通过结合安全几何探索与语义视觉推理，实现野外环境中的开放词汇物体搜索，显著提升导航效率和自主性。
摘要：
在复杂的、无结构的户外环境中实现自主导航需要机器人在没有先验地图和有限深度感知的情况下进行长时间范围的操作。在这种情况下，仅依赖几何前沿进行探索通常是不够的。在这种情况下，能够对前往何处以及哪些区域是安全的进行语义推理对于实现稳健、高效的探索至关重要。本文提出了WildOS，一个统一的系统，用于长时间范围的开放词汇物体搜索，结合了安全的几何探索与语义视觉推理。WildOS构建了一个稀疏的导航图以保持空间记忆，同时利用基于基础模型的视觉模块ExploRFM来评分图中的前沿节点。ExploRFM同时预测可通行性、视觉前沿和图像空间中的物体相似性，使机器人能够实时执行语义导航任务。所得到的视觉评分图使机器人能够在语义上有意义的方向上探索，同时确保几何安全性。此外，我们引入了一种基于粒子滤波的方法，用于开放词汇目标查询的粗略定位，该方法估计出机器人近景深度视界之外的候选目标位置，从而能够有效地向远距离目标规划。广泛的闭环现场实验在多样化的非铺装道路和城市地形上均表明，WildOS能够实现稳健的导航，其在效率和自主性方面均显著优于纯几何和纯视觉基线方法。我们的结果突显了视觉基础模型在驱动开放世界机器人行为方面的潜力，这些行为既具有语义信息又具有几何基础。项目页面：https://leggedrobotics.github.io/wildos/
英文摘要：
Autonomous navigation in complex, unstructured outdoor environments requires robots to operate over long ranges without prior maps and limited depth sensing. In such settings, relying solely on geometric frontiers for exploration is often insufficient. In such settings, the ability to reason semantically about where to go and what is safe to traverse is crucial for robust, efficient exploration. This work presents WildOS, a unified system for long-range, open-vocabulary object search that combines safe geometric exploration with semantic visual reasoning. WildOS builds a sparse navigation graph to maintain spatial memory, while utilizing a foundation-model-based vision module, ExploRFM, to score frontier nodes of the graph. ExploRFM simultaneously predicts traversability, visual frontiers, and object similarity in image space, enabling real-time, onboard semantic navigation tasks. The resulting vision-scored graph enables the robot to explore semantically meaningful directions while ensuring geometric safety. Furthermore, we introduce a particle-filter-based method for coarse localization of the open-vocabulary target query, that estimates candidate goal positions beyond the robot's immediate depth horizon, enabling effective planning toward distant goals. Extensive closed-loop field experiments across diverse off-road and urban terrains demonstrate that WildOS enables robust navigation, significantly outperforming purely geometric and purely vision-based baselines in both efficiency and autonomy. Our results highlight the potential of vision foundation models to drive open-world robotic behaviors that are both semantically informed and geometrically grounded. Project Page: https://leggedrobotics.github.io/wildos/
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。
