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 simulation 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 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。此外，我们开发了一种GPU加速的运动规划算法，该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，也为自主系统提供了实时的态势感知能力。仿真实验表明，我们的方法在峰值信噪比和重建误差方面与现有先进方法相当，但计算速度提升了数个数量级。在真实世界实验中，我们的算法获得了比现有先进方法构建的地图更优的地图质量（峰值信噪比至少高出0.8dB，几何重建精度高出16%以上），从而能够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我们的项目页面：https://tyuezhan.github.io/RT_GuIDE/