用分层规划提升机器人在未知环境中的实时高保真建模能力
HGS-Planner: Hierarchical Planning Framework for Active Scene Reconstruction Using 3D Gaussian Splatting
- 分层规划结合全局与局部策略，动态优化重建路径
- 通过完成度与质量增益评估，实现高效自适应重建
- 适用于搜救等复杂任务中的实时三维感知
在搜救等复杂任务中，机器人需在未知环境中做出智能决策，依赖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显著提升态势感知，对智能机器人至关重要。传统方法常因场景表示不佳或速度不足而难以满足实时需求。受3D高斯泼溅（3DGS）有效性的启发，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成度与质量增益，自适应引导重建过程，融合全局与局部规划以提升效率。在模拟与真实环境中的实验表明，本方法优于现有实时重建方法。
原文摘要 · Abstract (English)
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.
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