Dense map representation?

[wxyLuna2024]

Hi, First of all, thank you so much for creating a clean lidar-based SLAM feature that does not rely on IMU dependencies. I am runing lidar slam on a campus driving dataset. There are two things I found that leads to mapping error, therefore I am posting this and looking for suggestions"

1. Loop closure modified map ended at the first intersection, possibly leading to failure in detecting loop closure globally The blue path is the gt trajectory obtained from lio_sam. As seen from the screenshot, the green modified map ended at the first loop closure, while it's just halfway from finishing the whole route. I wonder which parameters I can adjust this by changing up the .yaml file or launch.py file to ensure modified mapping span through the entire bag file. I tried to adjust range of searching loop closure but does not seem to be very effective.

2. Missing a lot of points in the generated point cloud map

The accumulated point cloud from the right are from lio_sam.

The accumulated point cloud from the left are from lio_sam.

Since loop closure detection ended at the first route intersection, the mapping is incomplete. Also I recognize that the dense map representation is incomplete, as the road texture and height of building are all missing compare to those generated by lio_sam. Is there any parameters I could adjust to get a dense map representation?

I am looking for suggestions to solve these problem. Any help will be appreciated. PS: I would appreciate it if you can provide a citation for reference!