利用隐式场景表示的不确定性增强视觉导航的探索能力
Enhancing Exploratory Capability of Visual Navigation Using Uncertainty of Implicit Scene Representation
AI总结：
提出NUE流程，利用NeRF隐式场景表示的不确定性增强探索能力，并提取记忆信息辅助目标定位，实现端到端视觉导航，在图像目标导航任务上性能优于现有记忆型方法。
摘要：
在未知场景的视觉导航中，“探索”和“利用”同样至关重要。机器人必须首先通过探索建立环境认知，然后利用认知信息完成目标搜索。然而，大多数现有的图像目标导航方法优先考虑目标搜索，而忽视了探索行为的生成。为了解决这一问题，我们提出了基于不确定性驱动的探索导航（NUE）流程，该流程使用隐式且紧凑的场景表示——神经辐射场（NeRF）作为认知结构。我们估计NeRF的不确定性，并通过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，以增强机器人确定目标位置的推理能力。最终，我们将这两种能力无缝结合以产生导航动作。我们的流程是端到端的，环境认知结构在线构建。在图像目标导航上的大量实验结果表明，我们的流程能够增强探索行为，同时实现从探索阶段到利用阶段的自然过渡。这使得我们的模型在导航性能上优于现有的基于记忆的认知导航结构。
英文摘要：
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.
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。
