Diffusion models have been successfully applied to robotics problems such as manipulation and vehicle path planning. In this work, we explore their application to end-to-end navigation -- including both perception and planning -- by considering the problem of jointly performing global localization and path planning in known but arbitrary 2D environments. In particular, we introduce a diffusion model which produces collision-free paths in a global reference frame given an egocentric LIDAR scan, an arbitrary map, and a desired goal position. To this end, we implement diffusion in the space of paths in SE(2), and describe how to condition the denoising process on both obstacles and sensor observations. In our evaluation, we show that the proposed conditioning techniques enable generalization to realistic maps of considerably different appearance than the training environment, demonstrate our model's ability to accurately describe ambiguous solutions, and run extensive simulation experiments showcasing our model's use as a real-time, end-to-end localization and planning stack.

本研究解决了已知但任意的二维环境中联合全球定位和路径规划的问题。我们引入了一种扩散模型，能够根据自我中心的激光雷达扫描、任意地图和目标位置生成无碰撞路径。研究结果表明，所提出的条件化技术使模型能够在外观差异显著的现实地图上实现泛化，并有效描述模糊解，展示了其在实时定位和规划中的潜在应用价值。

利用扩散模型进行联合定位与规划