HGS-Planner: Hierarchical Planning Framework for Active Scene Reconstruction Using 3D Gaussian Splatting
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.
在复杂任务如搜救中，机器人必须在未知环境中做出智能决策，这依赖于其感知和理解周围环境的能力。高质量和实时的重建提升了情境感知能力，对智能机器人至关重要。传统方法通常难以提供良好的场景表示，或者速度过慢，无法用于实时操作。受3D高斯分布（3D Gaussian Splatting, 3DGS）高效性的启发，我们提出了一种用于快速且高保真主动重建的分层规划框架。我们的方法通过评估完成度和质量增益，自适应地引导重建过程，结合全局和局部规划以提高效率。在模拟和真实环境中的实验表明，我们的方法在性能上优于现有的实时方法。