用高斯点阵统一表示复杂室外环境，助力机器人实时自主导航
Gaussian Splatting as a Unified Representation for Autonomy in Unstructured Environments
- 用高斯点阵同时表征几何与外观信息，支持复杂场景建模
- 融合语义信息实现大规模任务驱动导航，提升环境理解能力
- 适用于无人结构化环境下的实时导航，适合自动驾驶研究者参考
本文主张高斯点阵是大型非结构化户外环境中自主机器人导航的合适统一表示。此类环境需要既能捕捉复杂结构，又具备实时导航计算效率的表示方式。我们证明，高斯点阵提供的密集几何与光度信息对非结构化环境中的导航非常有用。此外，可将语义信息嵌入高斯地图中，以实现大规模任务驱动的导航。通过实验，我们总结出该表示在机器人自主性应用中的若干挑战与机遇。
原文摘要 · Abstract (English)
In this work, we argue that Gaussian splatting is a suitable unified representation for autonomous robot navigation in large-scale unstructured outdoor environments. Such environments require representations that can capture complex structures while remaining computationally tractable for real-time navigation. We demonstrate that the dense geometric and photometric information provided by a Gaussian splatting representation is useful for navigation in unstructured environments. Additionally, semantic information can be embedded in the Gaussian map to enable large-scale task-driven navigation. From the lessons learned through our experiments, we highlight several challenges and opportunities arising from the use of such a representation for robot autonomy.
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