SOURCE-LINKED INTELLIGENCE

ReRadar: Robust Radar Global Localization via Rotation-Equivariant Descriptor Learning

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

Global localization with scanning millimeter-wave radar remains challenging because place-recognition descriptors often discard spatial structure needed for accurate pose retrieval. We present ReRadar, a radar global localization pipeline that extracts rotation-equivariant intermediate features using steerable convolutional neural networks, forms rotation-invariant descriptors through group pooling and NetVLAD aggregation, and combines descriptor retrieval with landmark-based matching to estimate the robot's three-degree-of-freedom (3-DoF) pose. Across fixed database-query evaluations, ReRadar

Read original source ↗ Open in workspace

recordType

paper

region

Global