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.

翻译：我们提出Splat-Nav导航流水线，包含实时安全规划模块和鲁棒状态估计模块，旨在高斯喷溅（GSplat）环境表征（计算机视觉领域新兴的流行3D场景表征）中运行。我们构建了可快速计算的严格碰撞约束，通过地图建立保证安全的多面体通道，并在此通道内优化B样条轨迹。同时，通过将GSplat表征解析为点云，开发了实时鲁棒状态估计模块：该模块使机器人无需先验知识即可通过点云配准从RGB-D图像定位全局位姿，随后利用图像到点云定位法从RGB图像追踪场景运动中的自身位姿。我们还向GSplat中融入语义信息以获取更优定位图像。所有模块主要在CPU上运行，释放GPU资源用于实时场景重建等任务。我们在仿真与硬件实验中验证了流水线的安全性与鲁棒性：重规划频率达5Hz，位姿估计频率达20Hz——较神经辐射场（NeRF）导航方法提升一个数量级，从而实现了实时导航。