Problem
Research questions and friction points this paper is trying to address.
Radar-based Navigation
Off-road Environments
Closed-loop Systems
Viewpoint-dependent Artifacts
DRT&R: Direct Radar Teach & Repeat
🤖 AI Summary
本文提出DRT&R，一种基于直接旋转雷达的导航方法，通过结合直接雷达处理与局部映射，在各种环境中实现厘米级定位。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Direct Radar Teach & Repeat
off-road navigation
real-time localization
radar-based
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