RLPR: Radar-to-LiDAR Place Recognition via Two-Stage Asymmetric Cross-Modal Alignment for Autonomous Driving

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. Radar is inherently weather-resilient; however, in scenarios where LiDAR-based maps already serve as the foundational infrastructure, constructing radar maps at scale would incur non-trivial additional overhead. To leverage the existing infrastructure with weather-robust onboard sensors, radar-to-LiDAR place recognition has garnered increasing interest. However, extracting discriminative and generalizable features shared between modalities remains challenging. In this work, we propose RLPR, a robust radar-to-LiDAR place recognition framework compatible with both scanning radars and phased-array radars. We design a dual-stream network that matches solely on shared spatial structures, deliberately discarding modality-specific signatures such as RCS and Doppler velocity in exchange for compatibility across radar types. 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 five datasets demonstrate that RLPR achieves state-of-the-art recognition accuracy with strong zero-shot generalization capabilities.