  This paper presents a real-time 3D mapping framework based on global matching
cost minimization and LiDAR-IMU tight coupling. The proposed framework
comprises a preprocessing module and three estimation modules: odometry
estimation, local mapping, and global mapping, which are all based on the tight
coupling of the GPU-accelerated voxelized GICP matching cost factor and the IMU
preintegration factor. The odometry estimation module employs a keyframe-based
fixed-lag smoothing approach for efficient and low-drift trajectory estimation,
with a bounded computation cost. The global mapping module constructs a factor
graph that minimizes the global registration error over the entire map with the
support of IMU constraints, ensuring robust optimization in feature-less
environments. The evaluation results on the Newer College dataset and KAIST
urban dataset show that the proposed framework enables accurate and robust
localization and mapping in challenging environments.
