用扩散模型同时完成定位与路径规划，端到端实现智能导航。
Joint Localization and Planning using Diffusion
- 在SE(2)空间中构建路径扩散模型，结合障碍物与激光扫描条件去噪
- 可在不同外观地图上泛化，准确生成存在歧义的多条可行路径
- 适合需要实时端到端导航的自动驾驶或机器人场景
扩散模型已在机器人操作和车辆路径规划中取得成功。本文探索其在端到端导航（包含感知与规划）中的应用，聚焦于在已知但任意二维环境下的全局定位与路径规划联合求解。提出一种扩散模型，给定自车视角的激光雷达扫描、任意地图及目标位置，可生成全局参考系下的无碰撞路径。为此，我们在SE(2)空间中实现扩散过程，并描述如何将去噪过程同时基于障碍物信息与传感器观测进行条件化。评估表明，所提条件化方法使模型能泛化至与训练环境外观差异显著的真实地图，展现对模糊解的准确描述能力，并通过大量仿真验证了该模型作为实时端到端定位与规划系统的能力。
原文摘要 · Abstract (English)
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.
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