Doppler-SLAM: Doppler-Aided Radar-Inertial and LiDAR-Inertial Simultaneous Localization and Mapping
Dong Wang, Hannes Haag, Daniel Casado Herraez, Stefan May, Cyrill Stachniss, Andreas Nuechter
AI summary
Problem
Traditional visual and LiDAR SLAM systems struggle in adverse weather, low light, and featureless environments, while existing radar-based methods often fail to fully exploit Doppler velocity data or handle dynamic scenes effectively.
Approach
The system tightly fuses IMU, 4D radar or FMCW LiDAR, and Doppler velocity measurements in a unified graph optimization framework, featuring a novel Doppler-based velocity filter for dynamic outlier removal and an online extrinsic calibration mechanism.
Key results
- Unified Doppler-aided radar-inertial and LiDAR-inertial SLAM framework
- Novel Doppler-based velocity filter for dynamic outlier removal without stationary assumptions
- Online extrinsic calibration mechanism using Doppler velocity and loop closure
- Significant accuracy and robustness improvements over state-of-the-art radar and LiDAR SLAM on public and proprietary datasets
Why it matters
It enables reliable autonomous navigation for ground vehicles in challenging environments like fog, rain, and dynamic traffic, advancing robust perception for real-world autonomous systems.
Abstract
Simultaneous localization and mapping is a criti- cal capability for autonomous systems. Traditional SLAM ap- proaches 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.