DRT&R：直接雷达教学与重复导航
DRT&R: Direct Radar Teach & Repeat
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中文总结 AI 辅助
本文提出DRT&R，一种结合直接雷达处理与局部建图的导航栈，在道路和越野环境中实现厘米级定位，并通过闭环测试验证实时性与最先进跟踪性能。
中文摘要 AI 辅助
基于雷达的导航因其对恶劣条件的鲁棒性而具有吸引力，这些条件包括降水、灰尘、雾和烟雾等空气中颗粒物，这些颗粒物可能导致基于激光雷达的系统失效。最近，保留并使用整个雷达扫描而非稀疏点的直接方法提高了道路上的全局定位性能。然而，这些方法尚未部署在越野环境或闭环系统中。此外，即使是直接全局地图也可能丢失信息：其全局性质导致视点相关的雷达伪影被平滑掉，而当建图和定位沿相似轨迹发生时，这些伪影可以提供位姿信息。本文介绍了直接雷达教学与重复导航（DRT&R）：一种基于直接旋转雷达的导航栈，通过将直接雷达处理与局部建图相结合，最大化保留的信息量。DRT&R在道路和越野环境中均实现了最先进的（SOTA）定位性能。使用344公里的道路数据和20公里的越野数据，DRT&R能够在大多数道路和越野条件下将定位误差控制在4厘米以内，在几何退化且稀疏的环境中控制在12厘米以内。DRT&R还使用Clearpath Warthog越野车辆，通过MPC控制器在闭环中自主评估超过10公里，证明其能够实时运行，并在越野雷达导航中实现SOTA跟踪性能。
英文摘要
Radar-based navigation is appealing for its robustness to adverse conditions involving airborne particles, such as precipitation, dust, fog, and smoke, that can cause lidar-based systems to fail. Recently, direct methods that retain and use the entire radar scan rather than sparse points have improved on-road global localization performance. However, they have yet to be deployed in off-road environments or in closed-loop systems. Additionally, even direct global maps may lose information: their global nature leads to a smoothing out of viewpoint-dependent radar artifacts, which can provide pose information when mapping and localization occur along similar trajectories. This paper introduces Direct Radar Teach & Repeat (DRT&R): a direct spinning radar-based navigation stack that maximizes the amount of retained information by combining direct radar processing with local mapping. DRT&R yields state-of-the-art (SOTA) localization performance in both on-road and off-road environments. Using 344 km of on-road data and 20 km of off-road data, DRT&R is able to localize to within 4 cm in most on-road and off-road conditions, and 12 cm in geometrically degenerate and sparse environments. DRT&R is also evaluated autonomously in closed loop with an MPC controller for more than 10 km using a Clearpath Warthog off-road vehicle, demonstrating that it runs in real time and achieves SOTA tracking performance for off-road radar navigation.
发表机构
- University of Toronto（多伦多大学）
- ETH Zürich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
