Pre-explored Semantic Maps, constructed through prior exploration using visual language models (VLMs), have proven effective as foundational elements for training-free robotic applications. However, existing approaches assume the map's accuracy and do not provide effective mechanisms for revising decisions based on incorrect maps. To address this, we introduce Context-Aware Replanning (CARe), which estimates map uncertainty through confidence scores and multi-view consistency, enabling the agent to revise erroneous decisions stemming from inaccurate maps without requiring additional labels. We demonstrate the effectiveness of our proposed method by integrating it with two modern mapping backbones, VLMaps and OpenMask3D, and observe significant performance improvements in object navigation tasks. More details can be found on the project page: https://care-maps.github.io/

翻译：通过视觉语言模型（VLM）进行先验探索构建的预探索语义地图，已被证明是免训练机器人应用的有效基础组件。然而，现有方法默认地图的准确性，并未提供基于错误地图修正决策的有效机制。为解决此问题，我们提出了上下文感知重规划（CARe）方法，该方法通过置信度分数与多视角一致性来估计地图的不确定性，使得智能体能够在无需额外标注的情况下，修正因地图不准确而产生的错误决策。我们将所提方法与两种现代建图骨干网络（VLMaps 和 OpenMask3D）集成，在目标导航任务中观察到显著的性能提升，从而验证了该方法的有效性。更多细节请参见项目页面：https://care-maps.github.io/