Doppler-SLAM: Doppler-Aided Radar-Inertial and LiDAR-Inertial Simultaneous Localization and Mapping

  • Simultaneous localization and mapping is a critical capability for autonomous systems. Traditional SLAM approaches often rely on visual or LiDAR sensors and face significant challenges in adverse conditions such as low light or featureless environments. To overcome these limitations, we propose a novel Doppler-aided radar-inertial and LiDAR-inertial SLAM framework that leverages the complementary strengths of 4D radar, FMCW LiDAR, and inertial measurement units. Our system integrates Doppler velocity measurements and spatial data into a tightly-coupled front-end and graph optimization back-end to provide enhanced ego velocity estimation, accurate odometry, and robust mapping. We also introduce a Doppler-based scan-matching technique to improve front-end odometry in dynamic environments. In addition, our framework incorporates an innovative online extrinsic calibration mechanism, utilizing Doppler velocity and loop closure to dynamically maintain sensor alignment. Extensive evaluations on both public and proprietary datasets show thatSimultaneous localization and mapping is a critical capability for autonomous systems. Traditional SLAM approaches often rely on visual or LiDAR sensors and face significant challenges in adverse conditions such as low light or featureless environments. To overcome these limitations, we propose a novel Doppler-aided radar-inertial and LiDAR-inertial SLAM framework that leverages the complementary strengths of 4D radar, FMCW LiDAR, and inertial measurement units. Our system integrates Doppler velocity measurements and spatial data into a tightly-coupled front-end and graph optimization back-end to provide enhanced ego velocity estimation, accurate odometry, and robust mapping. We also introduce a Doppler-based scan-matching technique to improve front-end odometry in dynamic environments. In addition, our framework incorporates an innovative online extrinsic calibration mechanism, utilizing Doppler velocity and loop closure to dynamically maintain sensor alignment. Extensive evaluations on both public and proprietary datasets show that our system significantly outperforms state-of-the-art radar-SLAM and LiDAR-SLAM frameworks in terms of accuracy and robustness. To encourage further research, the code of our Doppler-SLAM and our dataset are available at: https://github.com/Wayne-DWA/Doppler-SLAM .show moreshow less

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Metadaten
Author:Dong WangORCiD, Hannes Haag, Daniel Casado HerraezORCiD, Stefan May, Cyrill StachnissORCiD, Andreas NüchterORCiD
DOI:https://doi.org/10.1109/LRA.2025.3595071
ISSN:2377-3766
Parent Title (English):IEEE Robotics and Automation Letters
Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Document Type:Article
Language:English
Release Date:2025/12/11
Volume:10
Issue:9
Pagenumber:8
First Page:9438
Last Page:9445
institutes:Fakultät für Elektrotechnik Feinwerktechnik Informationstechnik
Research Themes:Digitalisierung & Künstliche Intelligenz
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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