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/

翻译：我们提出了一种主动建图与探索框架，该框架利用高斯溅射技术构建稠密地图。进一步，我们开发了一种GPU加速的运动规划算法，能够利用高斯地图进行实时导航。机器人机载构建的高斯地图在优化光度与几何质量的同时，实现了自主系统的实时态势感知。通过视点选择实验，我们证明本方法在获得与前沿方法相当的峰值信噪比和相近重建误差的同时，计算速度提升了数个数量级。在基于物理的闭环仿真与真实世界实验中，本算法实现了优于前沿方法的地图质量（峰值信噪比至少高出0.8dB，几何重建精度提升超过16%），并能够支持使用现成的开放集模型进行语义分割。实验视频及更多细节请访问项目页面：https://tyuezhan.github.io/RT GuIDE/