We propose a framework for active mapping and exploration that leverages Gaussian splatting for constructing information-rich maps. Further, we develop a parallelized 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 is competitive with approaches that use alternate information gain metrics, while being orders of magnitude faster to compute. In real-world experiments, our algorithm achieves better map quality (10% higher Peak Signal-to-Noise Ratio (PSNR) and 30% higher geometric reconstruction accuracy) than Gaussian maps constructed by traditional exploration baselines. Experiment videos and more details can be found on our project page: this https URL
我们提出了一个主动映射与探索框架，利用高斯分布（Gaussian Splatting）构建信息丰富的地图。此外，我们开发了一个并行化的运动规划算法，能够利用该高斯地图实现实时导航。该高斯地图在机器人上进行构建，并针对光度和几何质量进行优化，同时为自主导航提供实时的情境感知。通过模拟实验，我们证明了该方法与使用其他信息增益度量的方法相比具有竞争力，并且计算速度快了几个数量级。在真实环境实验中，我们的算法在地图质量上表现优异，比传统探索基线构建的高斯地图提高了10%的峰值信噪比（PSNR）和30%的几何重建精度。