# ReRadar：通过旋转等变描述子学习实现鲁棒雷达全局定位

> 原文链接：https://robotics.hellobike.com/papers/2026-09-16-2609-18092
> 频道：papers | 更新日期：2026-09-16 | 标签：arxiv, cs.RO
> 核心摘要：ReRadar针对扫描毫米波雷达全局定位，学习旋转等变特征与不变描述子，并在多个数据库-查询评测上验证。

# ReRadar：通过旋转等变描述子学习实现鲁棒雷达全局定位

::: v-pre
> 原文链接：[ReRadar: Robust Radar Global Localization via Rotation-Equivariant Descriptor Learning](https://arxiv.org/abs/2609.18092)（arXiv 2609.18092 · 发布日期：2026-09-16）

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

ReRadar针对扫描毫米波雷达全局定位，学习旋转等变特征与不变描述子，并在多个数据库-查询评测上验证。

## 核心要点

- 动机：现有地点识别描述子常丢弃空间结构，影响精确位姿检索。
- 方法：用可转向CNN提取旋转等变中间特征，经群池化和NetVLAD形成不变描述子。
- 定位：将描述子检索与基于地标的匹配结合，估计机器人3-DoF位姿。
- 结果：目标数据集适配在OORD、Mulran和降雪Boreas上取得高Recall@1和F1。

**验证范围与局限**：验证限于固定数据库-查询评测，覆盖OORD Bellmouth、Mulran DCC01和降雪Boreas序列。

## 英文摘要 (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.

作者：Duc Manh Nguyen, Truong Giang Dao, Gia Nghiem Luong, Hoang, Viet Trung, Anh Quang Nguyen
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## 原始参考来源 (Sources)

- [arXiv 2609.18092：ReRadar: Robust Radar Global Localization via Rotation-Equivariant Descriptor Learning](https://arxiv.org/abs/2609.18092)