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arXiv · 2609.18092

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

Duc Manh Nguyen·Truong Giang Dao·Gia Nghiem Luong·Viet Trung Hoang·Anh Quang Nguyen
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Abstract

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 with target-dataset adaptation achieves 99.37% Recall@1 on OORD Bellmouth, 91.44% Recall@1 with 80.99% F1_max on Mulran DCC01, and 99.38% Recall@1 on falling-snow Boreas sequence. Without target-dataset data, the cross-dataset model reaches 98.07% Recall@1 on OORD, performing comparably to the evaluated state-of-the-art methods.

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Duc Manh Nguyen, Truong Giang Dao, Gia Nghiem Luong, Viet Trung Hoang, Anh Quang Nguyen. 2026-09-16. ReRadar: Robust Radar Global Localization via Rotation-Equivariant Descriptor Learning. https://arxiv.org/abs/2609.18092

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