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

arXiv: https://arxiv.org/abs/2603.07920 · HTML: https://mlsanity.com/paper/2603.07920/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 37 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [MambaVision: A Hybrid Mamba-Transformer Vision Backbone](https://mlsanity.com/paper/2407.08083/) (2024)
- [PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition](https://mlsanity.com/paper/1804.03492/) (2018)
- [Boreas: A Multi-Season Autonomous Driving Dataset](https://mlsanity.com/paper/2203.10168/) (2022)
- [OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition](https://mlsanity.com/paper/2203.03397/) (2022)
- [Improving Point Cloud Based Place Recognition with Ranking-based Loss and Large Batch Training](https://mlsanity.com/paper/2203.00972/) (2022)
- [Global visual localization in LiDAR-maps through shared 2D-3D embedding space](https://mlsanity.com/paper/1910.04871/) (2019)
- [LIP-Loc: LiDAR Image Pretraining for Cross-Modal Localization](https://mlsanity.com/paper/2312.16648/) (2023)
- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data](https://mlsanity.com/paper/2302.01665/) (2023)
- [BEVPlace++: Fast, Robust, and Lightweight LiDAR Global Localization for Unmanned Ground Vehicles](https://mlsanity.com/paper/2408.01841/) (2024)
- [Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning](https://mlsanity.com/paper/2001.09438/) (2020)
- [AutoPlace: Robust Place Recognition with Single-chip Automotive Radar](https://mlsanity.com/paper/2109.08652/) (2021)
- [RaPlace: Place Recognition for Imaging Radar using Radon Transform and Mutable Threshold](https://mlsanity.com/paper/2307.04321/) (2023)
- [Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning](https://mlsanity.com/paper/2102.04960/) (2021)
- [Open-RadVLAD: Fast and Robust Radar Place Recognition](https://mlsanity.com/paper/2401.15380/) (2024)
- [RaLF: Flow-based Global and Metric Radar Localization in LiDAR Maps](https://mlsanity.com/paper/2309.09875/) (2023)
- [ModaLink: Unifying Modalities for Efficient Image-to-PointCloud Place Recognition](https://mlsanity.com/paper/2403.18762/) (2024)
- [SNAIL Radar: A large-scale diverse benchmark for evaluating 4D-radar-based SLAM](https://mlsanity.com/paper/2407.11705/) (2024)
- [HeRCULES: Heterogeneous Radar Dataset in Complex Urban Environment for Multi-session Radar SLAM](https://mlsanity.com/paper/2502.01946/) (2025)
- [Monocular Visual Place Recognition in LiDAR Maps via Cross-Modal State Space Model and Multi-View Matching](https://mlsanity.com/paper/2410.06285/) (2024)
- [SHeRLoc: Synchronized Heterogeneous Radar Place Recognition for Cross-Modal Localization](https://mlsanity.com/paper/2506.15175/) (2025)
- [ReFeree: Radar-Based Lightweight and Robust Localization using Feature and Free space](https://mlsanity.com/paper/2410.01325/) (2024)
- [LRFusionPR: A Polar BEV-Based LiDAR-Radar Fusion Network for Place Recognition](https://mlsanity.com/paper/2504.19186/) (2025)
- [ResLPR: A LiDAR Data Restoration Network and Benchmark for Robust Place Recognition Against Weather Corruptions](https://mlsanity.com/paper/2503.12350/) (2025)

## Abstract (arXiv)

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.