In the context of visual navigation in unknown scenes, both "exploration" and "exploitation" are equally crucial. Robots must first establish environmental cognition through exploration and then utilize the cognitive information to accomplish target searches. However, most existing methods for image-goal navigation prioritize target search over the generation of exploratory behavior. To address this, we propose the Navigation with Uncertainty-driven Exploration (NUE) pipeline, which uses an implicit and compact scene representation, NeRF, as a cognitive structure. We estimate the uncertainty of NeRF and augment the exploratory ability by the uncertainty to in turn facilitate the construction of implicit representation. Simultaneously, we extract memory information from NeRF to enhance the robot's reasoning ability for determining the location of the target. Ultimately, we seamlessly combine the two generated abilities to produce navigational actions. Our pipeline is end-to-end, with the environmental cognitive structure being constructed online. Extensive experimental results on image-goal navigation demonstrate the capability of our pipeline to enhance exploratory behaviors, while also enabling a natural transition from the exploration to exploitation phase. This enables our model to outperform existing memory-based cognitive navigation structures in terms of navigation performance.

翻译：在未知场景的视觉导航中，"探索"与"利用"同等重要。机器人需先通过探索建立环境认知，再利用认知信息完成目标搜索。然而，现有图像目标导航方法大多侧重目标搜索，而忽视探索行为的生成。为此，我们提出不确定性驱动探索导航（NUE）流程，采用隐式紧凑场景表示NeRF作为认知结构。我们估计NeRF的不确定性，并利用该不确定性增强探索能力，进而促进隐式表示的构建。同时，我们从NeRF中提取记忆信息，以提升机器人判断目标位置的推理能力。最终，我们将生成的两种能力无缝结合以产生导航动作。我们的流程为端到端系统，环境认知结构可在线构建。在图像目标导航任务上的大量实验结果表明，本流程能有效增强探索行为，并实现从探索阶段到利用阶段的自然过渡，使模型在导航性能上超越现有基于记忆的认知导航结构。