# Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information

arXiv: https://arxiv.org/abs/1909.03752 · HTML: https://mlsanity.com/paper/1909.03752/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 15 of 15)

- [OrienterNet: Visual Localization in 2D Public Maps with Neural Matching](https://mlsanity.com/paper/2304.02009/) (2023)
- [Deep Multi-Task Learning for Joint Localization, Perception, and Prediction](https://mlsanity.com/paper/2101.06720/) (2021)
- [Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors](https://mlsanity.com/paper/2203.03334/) (2022)
- [C-BEV: Contrastive Bird's Eye View Training for Cross-View Image Retrieval and 3-DoF Pose Estimation](https://mlsanity.com/paper/2312.08060/) (2023)
- [Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning](https://mlsanity.com/paper/2102.04960/) (2021)
- [RadarLoc: Learning to Relocalize in FMCW Radar](https://mlsanity.com/paper/2103.11562/) (2021)
- [Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities](https://mlsanity.com/paper/2112.04282/) (2021)
- [UnLoc: A Universal Localization Method for Autonomous Vehicles using LiDAR, Radar and/or Camera Input](https://mlsanity.com/paper/2307.00741/) (2023)
- [Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights](https://mlsanity.com/paper/2309.08731/) (2023)
- [RaNDT SLAM: Radar SLAM Based on Intensity-Augmented Normal Distributions Transform](https://mlsanity.com/paper/2408.11576/) (2024)
- [RaSCL: Radar to Satellite Crossview Localization](https://mlsanity.com/paper/2504.15899/) (2025)
- [RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment](https://mlsanity.com/paper/2604.13492/) (2026)
- [DPCN++: Differentiable Phase Correlation Network for Versatile Pose Registration](https://mlsanity.com/paper/2206.05707/) (2022)
- [RadarLCD: Learnable Radar-based Loop Closure Detection Pipeline](https://mlsanity.com/paper/2309.07094/) (2023)
- [Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization](https://mlsanity.com/paper/2605.07041/) (2026)

## Abstract (arXiv)

This paper presents an end-to-end radar odometry system which delivers robust, real-time pose estimates based on a learned embedding space free of sensing artefacts and distractor objects. The system deploys a fully differentiable, correlation-based radar matching approach. This provides the same level of interpretability as established scan-matching methods and allows for a principled derivation of uncertainty estimates. The system is trained in a (self-)supervised way using only previously obtained pose information as a training signal. Using 280km of urban driving data, we demonstrate that our approach outperforms the previous state-of-the-art in radar odometry by reducing errors by up 68% whilst running an order of magnitude faster.