# Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization?

arXiv: https://arxiv.org/abs/2203.10174 · HTML: https://mlsanity.com/paper/2203.10174/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2022.

## Built on by (most cited first, 9 of 9)

- [RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenes](https://mlsanity.com/paper/2506.01379/) (2025)
- [Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights](https://mlsanity.com/paper/2309.08731/) (2023)
- [RaSCL: Radar to Satellite Crossview Localization](https://mlsanity.com/paper/2504.15899/) (2025)
- [A Multi-modal Approach to Single-modal Visual Place Classification](https://mlsanity.com/paper/2305.06179/) (2023)
- [Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm](https://mlsanity.com/paper/2403.05666/) (2024)
- [RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation](https://mlsanity.com/paper/2604.04490/) (2026)
- [Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization](https://mlsanity.com/paper/2605.07041/) (2026)
- [The Coastline as a Structural Constraint: Harnessing Scene Geometry for Autonomous Surface Vessel Localization](https://mlsanity.com/paper/2608.21276/) (2026)
- [Automotive mmWave Spinning Radar Place Recognition with Spatially Gated Feature-Correlation Representation](https://mlsanity.com/paper/2609.27394/) (2026)

## Abstract (arXiv)

We present an extensive comparison between three topometric localization systems: radar-only, lidar-only, and a cross-modal radar-to-lidar system across varying seasonal and weather conditions using the Boreas dataset. Contrary to our expectations, our experiments showed that our lidar-only pipeline achieved the best localization accuracy even during a snowstorm. Our results seem to suggest that the sensitivity of lidar localization to moderate precipitation has been exaggerated in prior works. However, our radar-only pipeline was able to achieve competitive accuracy with a much smaller map. Furthermore, radar localization and radar sensors still have room to improve and may yet prove valuable in extreme weather or as a redundant backup system. Code for this project can be found at: https://github.com/utiasASRL/vtr3