Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps
<|reference_start|>Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps: We present Splat-Nav, a real-time navigation pipeline designed to work with environment representations generated by Gaussian Splatting (GSplat), a popular emerging 3D scene representation from computer vision. Splat-Nav consists of two components: 1) Splat-Plan, a safe planning module, and 2) Splat-Loc, a robust pose estimation module. Splat-Plan builds a safe-by-construction polytope corridor through the map based on mathematically rigorous collision constraints and then constructs a B\'ezier curve trajectory through this corridor. Splat-Loc provides a robust state estimation module, leveraging the point-cloud representation inherent in GSplat scenes for global pose initialization, in the absence of prior knowledge, and recursive real-time pose localization, given only RGB images. The most compute-intensive procedures in our navigation pipeline, such as the computation of the B\'ezier trajectories and the pose optimization problem run primarily on the CPU, freeing up GPU resources for GPU-intensive tasks, such as online training of Gaussian Splats. We demonstrate the safety and robustness of our pipeline in both simulation and hardware experiments, where we show online re-planning at 5 Hz and pose estimation at about 25 Hz, an order of magnitude faster than Neural Radiance Field (NeRF)-based navigation methods, thereby enabling real-time navigation.<|reference_end|>
@article{chen2024splat-nav:,
    title={Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps},
    author={Timothy Chen, Ola Shorinwa, Joseph Bruno, Javier Yu, Weijia Zeng,
  Keiko Nagami, Philip Dames, Mac Schwager},
    journal={arXiv preprint arXiv:2403.02751},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02751},
    primaryClass={cs.RO}
}