All-weather autonomy is critical for autonomous driving, which necessitates reliable localization across diverse scenarios. While LiDAR place recognition is widely deployed for this task, its performance degrades in adverse weather. Conversely, radar-based methods, though weather-resilient, are hindered by the general unavailability of radar maps. To bridge this gap, radar-to-LiDAR place recognition, which localizes radar scans within existing LiDAR maps, has garnered increasing interest. However, extracting discriminative and generalizable features shared between modalities remains challenging, compounded by the scarcity of large-scale paired training data and the signal heterogeneity across radar types. In this work, we propose RLPR, a robust radar-to-LiDAR place recognition framework compatible with single-chip, scanning, and 4D radars. We first design a dual-stream network to extract structural features that abstract away from sensor-specific signal properties (e.g., Doppler or RCS). Subsequently, motivated by our task-specific asymmetry observation between radar and LiDAR, we introduce a two-stage asymmetric cross-modal alignment (TACMA) strategy, which leverages the pre-trained radar branch as a discriminative anchor to guide the alignment process. Experiments on four datasets demonstrate that RLPR achieves state-of-the-art recognition accuracy with strong zero-shot generalization capabilities.

翻译：全天气环境自主性是自动驾驶的关键，这要求系统能在多种场景下实现可靠定位。激光雷达地点识别虽被广泛用于此任务，但其在恶劣天气下性能会下降。相反，基于雷达的方法虽对天气具有鲁棒性，却受限于雷达地图普遍不可用。为弥合这一差距，雷达-激光雷达地点识别（利用现有激光雷达地图对雷达扫描进行定位）日益受到关注。然而，提取跨模态间具有判别性和泛化性的共享特征仍面临挑战，加之大规模配对训练数据的匮乏以及不同雷达类型间的信号异质性，使问题更为复杂。本文提出RLPR——一种兼容单芯片雷达、扫描雷达和4D雷达的鲁棒雷达-激光雷达地点识别框架。我们首先设计双流网络，提取剥离传感器特定信号属性（如多普勒或RCS）的结构化特征；随后，基于对雷达与激光雷达间任务特定非对称性的观察，引入两阶段非对称跨模态对齐策略，该策略利用预训练的雷达分支作为判别性锚点引导对齐过程。在四个数据集上的实验表明，RLPR在实现最先进识别精度的同时，具备强大的零样本泛化能力。