超越不确定性：基于FisherRF的安全机器人导航与3D场景理解的风险感知主动视图获取
Beyond Uncertainty: Risk-Aware Active View Acquisition for Safe Robot Navigation and 3D Scene Understanding with FisherRF
AI总结：
针对机器人导航中主动视图获取的效率与安全问题，提出结合FisherRF的RaEM框架，通过一致性风险度量优先处理安全关键区域以选择下一个最佳视图，实现安全与重建效率双提升，经高保真实验验证有效。
摘要：
主动视图获取问题已在使用NeRF（神经辐射场）和3D Gaussian Splatting（3D高斯溅射）的机器人导航场景中得到广泛研究。为提升场景重建效率并保障机器人安全，我们提出风险感知环境掩蔽（RaEM）框架。RaEM利用一致性风险度量动态优先处理未知环境中的安全关键区域，引导主动视图获取算法确定下一个最佳视图（NBV）。该框架与FisherRF（通过最大化预期信息增益选择NBV的方法）集成后，实现双重目标：提升机器人安全性，同时提高风险感知3D场景重建与理解的效率。大量高保真实验验证了该方法的有效性，证明其能为主动机器人探索与3D场景理解构建一个鲁棒且以安全为核心的框架。
英文摘要：
The active view acquisition problem has been extensively studied in the context of robot navigation using NeRF and 3D Gaussian Splatting. To enhance scene reconstruction efficiency and ensure robot safety, we propose the Risk-aware Environment Masking (RaEM) framework. RaEM leverages coherent risk measures to dynamically prioritize safety-critical regions of the unknown environment, guiding active view acquisition algorithms toward identifying the next-best-view (NBV). Integrated with FisherRF, which selects the NBV by maximizing expected information gain, our framework achieves a dual objective: improving robot safety and increasing efficiency in risk-aware 3D scene reconstruction and understanding. Extensive high-fidelity experiments validate the effectiveness of our approach, demonstrating its ability to establish a robust and safety-focused framework for active robot exploration and 3D scene understanding.
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。
