This work proposes a novel approach to bolster both the robot's risk assessment and safety measures while deepening its understanding of 3D scenes, which is achieved by leveraging Radiance Field (RF) models and 3D Gaussian Splatting. To further enhance these capabilities, we incorporate additional sampled views from the environment with the RF model. One of our key contributions is the introduction of Risk-aware Environment Masking (RaEM), which prioritizes crucial information by selecting the next-best-view that maximizes the expected information gain. This targeted approach aims to minimize uncertainties surrounding the robot's path and enhance the safety of its navigation. Our method offers a dual benefit: improved robot safety and increased efficiency in risk-aware 3D scene reconstruction and understanding. Extensive experiments in real-world scenarios demonstrate the effectiveness of our proposed approach, highlighting its potential to establish a robust and safety-focused framework for active robot exploration and 3D scene understanding.

翻译：本文提出了一种新颖方法，通过利用辐射场模型与三维高斯溅射技术，在强化机器人风险评估与安全措施的同时，深化其对三维场景的理解。为增强上述能力，我们基于辐射场模型引入额外从环境采集的采样视角。核心贡献之一是提出风险感知环境掩码技术——通过选取能最大化预期信息增益的最优下一视角来优先处理关键信息。这种定向方法旨在最小化机器人路径相关的不确定性，提升其导航安全性。本方法兼具双重优势：提升机器人安全性，并提高风险感知三维场景重建与理解的效率。真实场景的大量实验验证了该方法的有效性，凸显其在构建以安全为核心的主动机器人探索与三维场景理解框架方面的潜力。