SAFER-Splat (Simultaneous Action Filtering and Environment Reconstruction) is a real-time, scalable, and minimally invasive action filter, based on control barrier functions, for safe robotic navigation in a detailed map constructed at runtime using Gaussian Splatting (GSplat). We propose a novel Control Barrier Function (CBF) that not only induces safety with respect to all Gaussian primitives in the scene, but when synthesized into a controller, is capable of processing hundreds of thousands of Gaussians while maintaining a minimal memory footprint and operating at 15 Hz during online Splat training. Of the total compute time, a small fraction of it consumes GPU resources, enabling uninterrupted training. The safety layer is minimally invasive, correcting robot actions only when they are unsafe. To showcase the safety filter, we also introduce SplatBridge, an open-source software package built with ROS for real-time GSplat mapping for robots. We demonstrate the safety and robustness of our pipeline first in simulation, where our method is 20-50x faster, safer, and less conservative than competing methods based on neural radiance fields. Further, we demonstrate simultaneous GSplat mapping and safety filtering on a drone hardware platform using only on-board perception. We verify that under teleoperation a human pilot cannot invoke a collision. Our videos and codebase can be found at this https URL.
SAFER-Splat（Simultaneous Action Filtering and Environment Reconstruction）是一种基于控制屏障函数的实时、可扩展且最小干预的动作过滤器，用于在运行时使用高斯投影（GSplat）构建的详细地图中实现安全的机器人导航。我们提出了一种新颖的控制屏障函数（Control Barrier Function, CBF），该函数不仅在场景中的所有高斯基元上引入了安全性，还能够合成到控制器中，以处理数十万个高斯点，同时保持最小的内存占用，并在在线 Splat 训练期间以 15 Hz 的频率运行。在总计算时间中，只有一小部分消耗了 GPU 资源，从而实现了不中断的训练。安全层的干预极少，仅在机器人动作不安全时进行修正。 为了展示这一安全过滤器，我们还推出了 SplatBridge，一个基于 ROS 的开源软件包，专为机器人实时 GSplat 映射而设计。我们首先在仿真中展示了管道的安全性和鲁棒性，结果表明，我们的方法比基于神经辐射场的竞争方法快 20-50 倍，且更安全、更不保守。进一步地，我们在无人机硬件平台上展示了同时进行 GSplat 映射和安全过滤，使用的仅是机载感知系统。我们验证了在遥控操作下，人工飞行员无法引发碰撞。