ICRA23_4DRadarSLAM A 4D Imaging Radar SLAM System for Large-scale Environments based on Pose Graph

Title: 4DRadarSLAM: A 4D Imaging Radar SLAM System for Large-scale
Environments based on Pose Graph Optimization
Abs:
LiDAR-based SLAM may easily fail in adverse
weathers (e.g., rain, snow, smoke, fog), while mmWave Radar
remains unaffected. However, current researches are primarily
focused on 2D (x, y) or 3D (x, y, doppler) Radar and 3D LiDAR,
while limited work can be found for 4D Radar (x, y, z, doppler).
As a new entrant to the market with unique characteristics, 4D
Radar outputs 3D point cloud with added elevation information,
rather than 2D point cloud; compared with 3D LiDAR, 4D
Radar has noisier and sparser point cloud, making it more
challenging to extract geometric features (edge and plane). In
this paper, we propose a full system for 4D Radar SLAM
consisting of three modules: 1) Front-end module performs
scan-to-scan matching to calculate the odometry based on
GICP, considering the probability distribution of each point;
2) Loop detection utilizes multiple rule-based loop pre-filtering
steps, followed by an intensity scan context step to identify
loop candidates, and odometry check to reject false loop; 3)
Back-end builds a pose graph using front-end odometry, loop
closure, and optional GPS data. Optimal pose is achieved
through g2o. We conducted real experiments on two platforms
and five datasets (ranging from 240m to 4.8km) and will
make the code open-source to promote further research at:
github.com/zhuge2333/4DRadarSLAM