RadarSLAM: A robust simultaneous localization and mapping system for all weather conditions

Ziyang Hong, Yvan Petillot, Andrew Wallace, Sen Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
58 Downloads (Pure)


A Simultaneous Localization and Mapping (SLAM) system must be robust to support long-term mobile vehicle and robot applications. However, camera and LiDAR based SLAM systems can be fragile when facing challenging illumination or weather conditions which degrade the utility of imagery and point cloud data. Radar, whose operating electromagnetic spectrum is less affected by environmental changes, is promising although its distinct sensor model and noise characteristics bring open challenges when being exploited for SLAM. This paper studies the use of a Frequency Modulated Continuous Wave radar for SLAM in large-scale outdoor environments. We propose a full radar SLAM system, including a novel radar motion estimation algorithm that leverages radar geometry for reliable feature tracking. It also optimally compensates motion distortion and estimates pose by joint optimization. Its loop closure component is designed to be simple yet efficient for radar imagery by capturing and exploiting structural information of the surrounding environment. Extensive experiments on three public radar datasets, ranging from city streets and residential areas to countryside and highways, show competitive accuracy and reliability performance of the proposed radar SLAM system compared to the state-of-the-art LiDAR, vision and radar methods. The results show that our system is technically viable in achieving reliable SLAM in extreme weather conditions on the RADIATE Dataset, for example, heavy snow and dense fog, demonstrating the promising potential of using radar for all-weather localization and mapping.

Original languageEnglish
Pages (from-to)519-542
Number of pages24
JournalInternational Journal of Robotics Research
Issue number5
Early online date24 Apr 2022
Publication statusPublished - Apr 2022


  • all-weather perception
  • radar sensing
  • simultaneous localization and mapping

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Mechanical Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Applied Mathematics


Dive into the research topics of 'RadarSLAM: A robust simultaneous localization and mapping system for all weather conditions'. Together they form a unique fingerprint.

Cite this