# RaSCL: Radar to Satellite Crossview Localization

arXiv: https://arxiv.org/abs/2504.15899 · HTML: https://mlsanity.com/paper/2504.15899/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 48 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [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)
- [Uncertainty-aware Vision-based Metric Cross-view Geolocalization](https://mlsanity.com/paper/2211.12145/) (2022)
- [Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments](https://mlsanity.com/paper/1711.06623/) (2017)
- [RSL-Net: Localising in Satellite Images From a Radar on the Ground](https://mlsanity.com/paper/2001.03233/) (2020)
- [UAV Localization Using Autoencoded Satellite Images](https://mlsanity.com/paper/2102.05692/) (2021)
- [Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar](https://mlsanity.com/paper/2001.10789/) (2020)
- [Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information](https://mlsanity.com/paper/1909.03752/) (2019)
- [Cross-view geo-localization: a survey](https://mlsanity.com/paper/2406.09722/) (2024)
- [Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning](https://mlsanity.com/paper/2105.14152/) (2021)
- [Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors](https://mlsanity.com/paper/2203.03334/) (2022)
- [Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization?](https://mlsanity.com/paper/2203.10174/) (2022)
- [Lidar-level localization with radar? The CFEAR approach to accurate, fast and robust large-scale radar odometry in diverse environments](https://mlsanity.com/paper/2211.02445/) (2022)
- [Radar-only ego-motion estimation in difficult settings via graph matching](https://mlsanity.com/paper/1904.11476/) (2019)
- [Radar SLAM: A Robust SLAM System for All Weather Conditions](https://mlsanity.com/paper/2104.05347/) (2021)
- [Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry](https://mlsanity.com/paper/2311.11260/) (2023)
- [Overhead Image Factors for Underwater Sonar-based SLAM](https://mlsanity.com/paper/2202.05811/) (2022)
- [Are Doppler Velocity Measurements Useful for Spinning Radar Odometry?](https://mlsanity.com/paper/2404.01537/) (2024)
- [Get It For Free: Radar Segmentation without Expert Labels and Its Application in Odometry and Localization](https://mlsanity.com/paper/2409.18434/) (2024)
- [LodeStar: Maritime Radar Descriptor for Semi-Direct Radar Odometry](https://mlsanity.com/paper/2403.02773/) (2024)
- [Radar Teach and Repeat: Architecture and Initial Field Testing](https://mlsanity.com/paper/2409.10491/) (2024)
- [The Finer Points: A Systematic Comparison of Point-Cloud Extractors for Radar Odometry](https://mlsanity.com/paper/2409.12256/) (2024)

## Built on by (most cited first, 1 of 1)

- [The Coastline as a Structural Constraint: Harnessing Scene Geometry for Autonomous Surface Vessel Localization](https://mlsanity.com/paper/2608.21276/) (2026)

## Abstract (arXiv)

GNSS is unreliable, inaccurate, and insufficient in many real-time autonomous field applications. In this work, we present a GNSS-free global localization solution that contains a method of registering imaging radar on the ground with overhead RGB imagery, with joint optimization of relative poses from odometry and global poses from our overhead registration. Previous works have used various combinations of ground sensors and overhead imagery, and different feature extraction and matching methods. These include various handcrafted and deep-learning-based methods for extracting features from overhead imagery. Our work presents insights on extracting essential features from RGB overhead images for effective global localization against overhead imagery using only ground radar and a single georeferenced initial guess. We motivate our method by evaluating it on datasets in diverse geographic conditions and robotic platforms, including on an Unmanned Surface Vessel (USV) as well as urban and suburban driving datasets.