# RaLF: Flow-based Global and Metric Radar Localization in LiDAR Maps

arXiv: https://arxiv.org/abs/2309.09875 · HTML: https://mlsanity.com/paper/2309.09875/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 39 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](https://mlsanity.com/paper/1909.01300/) (2019)
- [Boreas: A Multi-Season Autonomous Driving Dataset](https://mlsanity.com/paper/2203.10168/) (2022)
- [OverlapNet: Loop Closing for LiDAR-based SLAM](https://mlsanity.com/paper/2105.11344/) (2021)
- [LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM](https://mlsanity.com/paper/2103.05056/) (2021)
- [Deep Auxiliary Learning for Visual Localization and Odometry](https://mlsanity.com/paper/1803.03642/) (2018)
- [Global visual localization in LiDAR-maps through shared 2D-3D embedding space](https://mlsanity.com/paper/1910.04871/) (2019)
- [DiSCO: Differentiable Scan Context with Orientation](https://mlsanity.com/paper/2010.10949/) (2020)
- [Learning to Localize Using a LiDAR Intensity Map](https://mlsanity.com/paper/2012.10902/) (2020)
- [CMRNet: Camera to LiDAR-Map Registration](https://mlsanity.com/paper/1906.10109/) (2019)
- [Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning](https://mlsanity.com/paper/2203.01578/) (2022)
- [AutoPlace: Robust Place Recognition with Single-chip Automotive Radar](https://mlsanity.com/paper/2109.08652/) (2021)
- [CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps](https://mlsanity.com/paper/2004.13795/) (2020)
- [PADLoC: LiDAR-Based Deep Loop Closure Detection and Registration Using Panoptic Attention](https://mlsanity.com/paper/2209.09699/) (2022)
- [CoVIO: Online Continual Learning for Visual-Inertial Odometry](https://mlsanity.com/paper/2303.10149/) (2023)
- [RaPlace: Place Recognition for Imaging Radar using Radon Transform and Mutable Threshold](https://mlsanity.com/paper/2307.04321/) (2023)
- [RaLL: End-to-end Radar Localization on Lidar Map Using Differentiable Measurement Model](https://mlsanity.com/paper/2009.07061/) (2020)
- [Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning](https://mlsanity.com/paper/2102.04960/) (2021)
- [Contrastive Learning for Unsupervised Radar Place Recognition](https://mlsanity.com/paper/2110.02744/) (2021)

## Built on by (most cited first, 5 of 5)

- [CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration](https://mlsanity.com/paper/2402.00129/) (2024)
- [Visual-Inertial SLAM for Unstructured Outdoor Environments: Benchmarking the Benefits and Computational Costs of Loop Closing](https://mlsanity.com/paper/2408.01716/) (2024)
- [RLPR: Radar-to-LiDAR Place Recognition via Two-Stage Asymmetric Cross-Modal Alignment for Autonomous Driving](https://mlsanity.com/paper/2603.07920/) (2026)
- [4DRaL: Bridging 4D Radar with LiDAR for Place Recognition using Knowledge Distillation](https://mlsanity.com/paper/2603.26206/) (2026)
- [Joint Target-Less Intrinsic and Extrinsic Camera-LiDAR Calibration using Deep Point Correspondences](https://mlsanity.com/paper/2605.23397/) (2026)

## Abstract (arXiv)

Localization is paramount for autonomous robots. While camera and LiDAR-based approaches have been extensively investigated, they are affected by adverse illumination and weather conditions. Therefore, radar sensors have recently gained attention due to their intrinsic robustness to such conditions. In this paper, we propose RaLF, a novel deep neural network-based approach for localizing radar scans in a LiDAR map of the environment, by jointly learning to address both place recognition and metric localization. RaLF is composed of radar and LiDAR feature encoders, a place recognition head that generates global descriptors, and a metric localization head that predicts the 3-DoF transformation between the radar scan and the map. We tackle the place recognition task by learning a shared embedding space between the two modalities via cross-modal metric learning. Additionally, we perform metric localization by predicting pixel-level flow vectors that align the query radar scan with the LiDAR map. We extensively evaluate our approach on multiple real-world driving datasets and show that RaLF achieves state-of-the-art performance for both place recognition and metric localization. Moreover, we demonstrate that our approach can effectively generalize to different cities and sensor setups than the ones used during training. We make the code and trained models publicly available at http://ralf.cs.uni-freiburg.de.