实时构建高精度地图并快速导航，适合机器人自主探索。
RT-GuIDE: Real-Time Gaussian Splatting for Information-Driven Exploration
- 用高斯点云构建稠密地图，兼顾视觉与几何质量。
- 比顶尖方法快数个量级，闭环实验中地图质量提升0.8dB以上。
- 适用于需实时感知的机器人探索任务，支持开集语义分割。
我们提出一种主动建图与探索框架，利用高斯点云构建稠密地图，并开发了基于GPU加速的运动规划算法，实现实时导航。机器人本地构建的高斯地图在保持光度与几何质量的同时，支持实时情境感知以实现自主运行。视点选择实验表明，本方法在峰值信噪比（PSNR）和重建误差方面与当前最先进方法相当，但计算速度提升数个数量级。在闭环物理仿真与真实世界实验中，本算法生成的地图质量优于最先进方法至少0.8dB PSNR，几何重建准确率提升超过16%，并支持使用现成的开集语义分割模型。
原文摘要 · Abstract (English)
We propose a framework for active mapping and exploration that leverages Gaussian splatting for constructing dense maps. Further, we develop a GPU-accelerated motion planning algorithm that can exploit the Gaussian map for real-time navigation. The Gaussian map constructed onboard the robot is optimized for both photometric and geometric quality while enabling real-time situational awareness for autonomy. We show through viewpoint selection experiments that our method yields comparable Peak Signal-to-Noise Ratio (PSNR) and similar reconstruction error to state-of-the-art approaches, while being orders of magnitude faster to compute. In closed-loop physics-based simulation and real-world experiments, our algorithm achieves better map quality (at least 0.8dB higher PSNR and more than 16% higher geometric reconstruction accuracy) than maps constructed by a state-of-the-art method, enabling semantic segmentation using off-the-shelf open-set models. Experiment videos and more details can be found on our project page: https://tyuezhan.github.io/RT GuIDE/
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