In complex missions such as search and rescue,robots must make intelligent decisions in unknown environments, relying on their ability to perceive and understand their surroundings. High-quality and real-time reconstruction enhances situational awareness and is crucial for intelligent robotics. Traditional methods often struggle with poor scene representation or are too slow for real-time use. Inspired by the efficacy of 3D Gaussian Splatting (3DGS), we propose a hierarchical planning framework for fast and high-fidelity active reconstruction. Our method evaluates completion and quality gain to adaptively guide reconstruction, integrating global and local planning for efficiency. Experiments in simulated and real-world environments show our approach outperforms existing real-time methods.

翻译：在搜救等复杂任务中，机器人必须在未知环境中做出智能决策，这依赖于其对周围环境的感知与理解能力。高质量且实时的场景重建能够增强态势感知能力，对智能机器人技术至关重要。传统方法常因场景表征质量不佳或实时性不足而难以适用。受3D高斯溅射（3DGS）高效性的启发，我们提出一种用于快速高保真主动重建的分层规划框架。该方法通过评估场景完整度与质量增益来自适应引导重建过程，并融合全局与局部规划以提升效率。在仿真与真实环境中的实验表明，本方法优于现有的实时重建方法。