# RadarSLAM: Radar based Large-Scale SLAM in All Weathers

arXiv: https://arxiv.org/abs/2005.02198 · HTML: https://mlsanity.com/paper/2005.02198/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2020.

## Built on by (most cited first, 14 of 14)

- [Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension](https://mlsanity.com/paper/2405.12821/) (2024)
- [Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning](https://mlsanity.com/paper/2102.04960/) (2021)
- [Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry](https://mlsanity.com/paper/2311.11260/) (2023)
- [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)
- [Addressing the challenges of loop detection in agricultural environments](https://mlsanity.com/paper/2408.15761/) (2024)
- [Holistic Fusion: Task- and Setup-Agnostic Robot Localization and State Estimation with Factor Graphs](https://mlsanity.com/paper/2504.06479/) (2025)
- [RADLER: Radar Object Detection Leveraging Semantic 3D City Models and Self-Supervised Radar-Image Learning](https://mlsanity.com/paper/2504.12167/) (2025)
- [RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment](https://mlsanity.com/paper/2604.13492/) (2026)
- [RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Weather](https://mlsanity.com/paper/2112.02469/) (2021)
- [RadarLCD: Learnable Radar-based Loop Closure Detection Pipeline](https://mlsanity.com/paper/2309.07094/) (2023)
- [Robust Vehicle Localization and Tracking in Rain using Street Maps](https://mlsanity.com/paper/2409.01038/) (2024)
- [Automotive mmWave Spinning Radar Place Recognition with Spatially Gated Feature-Correlation Representation](https://mlsanity.com/paper/2609.27394/) (2026)

## Abstract (arXiv)

Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem. In this paper, RadarSLAM, a full radar based graph SLAM system, is proposed for reliable localization and mapping in large-scale environments. It is composed of pose tracking, local mapping, loop closure detection and pose graph optimization, enhanced by novel feature matching and probabilistic point cloud generation on radar images. Extensive experiments are conducted on a public radar dataset and several self-collected radar sequences, demonstrating the state-of-the-art reliability and localization accuracy in various adverse weather conditions, such as dark night, dense fog and heavy snowfall.