SOURCE-LINKED INTELLIGENCE

DRT&R: Direct Radar Teach & Repeat

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

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 pos

Read original source ↗ Open in workspace

recordType

paper

region

Global