We present Splat-Nav, a navigation pipeline that consists of a real-time safe planning module and a robust state estimation module designed to operate in the Gaussian Splatting (GSplat) environment representation, a popular emerging 3D scene representation from computer vision. We formulate rigorous collision constraints that can be computed quickly to build a guaranteed-safe polytope corridor through the map. We then optimize a B-spline trajectory through this corridor. We also develop a real-time, robust state estimation module by interpreting the GSplat representation as a point cloud. The module enables the robot to localize its global pose with zero prior knowledge from RGB-D images using point cloud alignment, and then track its own pose as it moves through the scene from RGB images using image-to-point cloud localization. We also incorporate semantics into the GSplat in order to obtain better images for localization. All of these modules operate mainly on CPU, freeing up GPU resources for tasks like real-time scene reconstruction. We demonstrate the safety and robustness of our pipeline in both simulation and hardware, where we show re-planning at 5 Hz and pose estimation at 20 Hz, an order of magnitude faster than Neural Radiance Field (NeRF)-based navigation methods, thereby enabling real-time navigation.
我们介绍了Splat-Nav，一种导航流程，它包括一个实时安全规划模块和一个鲁棒状态估计模块，这两个模块设计用于在高斯Splatting（GSplat）环境表示中运行，GSplat是计算机视觉中流行的新兴3D场景表示方法。我们制定了可以快速计算的严格碰撞约束，以通过地图构建一个保证安全的多面体走廊。然后，我们通过这个走廊优化一个B样条轨迹。我们还通过将GSplat表示解释为点云，开发了一个实时、鲁棒的状态估计模块。该模块使机器人能够利用点云对齐从RGB-D图像中，无需任何先验知识，定位其全局姿态，然后利用图像到点云的定位，跟踪它在场景中移动的姿态。我们还将语义信息整合到GSplat中，以获得更好的定位图像。所有这些模块主要在CPU上运行，为像实时场景重建这样的任务释放GPU资源。我们在模拟和硬件中展示了我们流程的安全性和鲁棒性，在这里我们展示了以5 Hz的速度重新规划和以20 Hz的速度估计姿态，比基于神经辐射场（NeRF）的导航方法快一个数量级，从而实现了实时导航。