Simultaneous localization and mapping (SLAM) is a critical capability for autonomous systems. Traditional SLAM approaches, which often rely on visual or LiDAR sensors, 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.

翻译：同步定位与建图（SLAM）是自主系统的关键能力。传统的SLAM方法通常依赖视觉或激光雷达传感器，在低光照或无特征环境等恶劣条件下面临重大挑战。为克服这些限制，我们提出了一种新颖的多普勒辅助雷达-惯性及激光雷达-惯性SLAM框架，该框架利用了4D雷达、FMCW激光雷达和惯性测量单元的互补优势。我们的系统将多普勒速度测量与空间数据集成到一个紧耦合的前端和图优化后端中，以提供增强的自车速度估计、精确的里程计和鲁棒的建图。我们还引入了一种基于多普勒的扫描匹配技术，以改善动态环境中的前端里程计精度。此外，我们的框架融合了一种创新的在线外参标定机制，利用多普勒速度与闭环检测动态维持传感器间的对齐。在公开及专有数据集上的大量评估表明，我们的系统在精度与鲁棒性方面显著优于当前最先进的雷达SLAM与激光雷达SLAM框架。为促进进一步研究，我们的Doppler-SLAM代码及数据集发布于：https://github.com/Wayne-DWA/Doppler-SLAM。