QE4DRaSLAM: Data-Augmented 4-D mmWave SLAM for Extremely Sparse Point Clouds

Autonomous navigation of robots has entered an era of robustness. Therefore, the demand for stable navigation in extreme environments is increasing. 4-D millimeter-wave radar has a strong ability to resist environmental interference and can maintain accurate perception even in extreme weather conditions. It is an excellent solution. However, current research on navigation mainly focuses on vision-based and LiDAR-based methods, and the attention paid to 4-D millimeter-wave radar (x, y, z, Doppler) radar systems is very limited. The current 4-D millimeter wave simultaneous localization and mapping (SLAM) system treats 4-D millimeter wave data as LiDAR data and directly processes point cloud data. At the same time, the 4-D millimeter wave radar point cloud has large noise, strong sparsity, and difficult to extract geometric features, such as edges and planes, which poses a challenge to the establishment of a high-precision SLAM system. In addition, the data volume and cost of 4-D millimeter wave radar are directly related. The cost of tens of thousands of dollars for high-end 4-D millimeter wave radar directly restricts its application. Therefore, the construction of a SLAM system using low-cost and low-data-volume 4-D millimeter wave radar has become an urgent problem to be solved. To address these problems, this article proposes a data-enhanced low-cost 4-D millimeter-wave radar SLAM system. The system introduces: 1) a point cloud SLAM quality assessment framework to establish the relationship between data quality and SLAM performance; 2) a data enhancement technique based on quality assessment to generate high-quality data from sparse, low-quality 4-D radar input to meet positioning and mapping requirements; and 3) a SLAM system enhanced by quality characteristics, incorporating velocity calibration into scan matching to improve accuracy, and fusing inertial measurement unit (IMU) preintegration with Doppler velocity preintegration to obtain a robust integration factor. In addition, a velocity segment-based loop closure mechanism is constructed in the SLAM system. Verification experiments conducted in real environments with dataset comparisons covering distances from 180 m to 4.8 km have demonstrated the scientificity and effectiveness of our approach, making a significant contribution to the promotion of low-cost 4-D millimeter-wave radar applications.