In that example, the ground-truth rotation from the expensive GPS+INS system is injected into the propagtaion step.
As you will see below, without the external rotation source, the imu itself quickly diverges.
I simply replaced it by scan-to-scan registration from LiDAR sensor (with Open3D ICP). Then, the corrected PVA (position, velocity, and attitude) is set as the estimator's newer state. I think this can be said loosely coupled lidar-inertial odometry.
The point is: LiDAR and IMU help each other.
A LiDAR prevents an IMU diverges.
The IMU provides a good initial guess for the LiDAR scan matching.
and then, the LiDAR again better prevents an IMU diverges... and go on and on ...
This simple project, which only used Python and a few files, is intended to be educational.
I expect, after playing it, a reader could be able to answer why IMU and LiDAR should be fusioned.