HViktorTsoi / PV-LIO

A probabilistic voxelmap-based LiDAR-Inertial Odometry.
GNU General Public License v2.0
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Reducing Drift with High Compute #12

Open satyajitghana opened 1 year ago

satyajitghana commented 1 year ago

Hi,

I am trying to use PV-LIO on a scan with a lot of very small rooms. The thing is i don't want realtime mapping, but i want the drift to be as less as possible. I can run the algorithm on 96 cores / 192 cores with 1024GB of RAM. What are the parameters that i need to tune so it makes use of all the compute available?

here's what i tried

So what parameters should i try to tune?

I have already tried things like SC (Scan Context) and LC (Loop Closure) algorithms (https://github.com/gisbi-kim/FAST_LIO_SLAM) that were suited for FASTLIO2 but they work well with PV-LIO https://github.com/hku-mars/Point-LIO as well. but they also are not able to fix the drift.

Also I've tried HBA (Hierarchical Bundle Adjustment) (https://github.com/hku-mars/HBA) , it does solve the drift, but it adds too much noise to the point cloud to a point that the point cloud is unusable.

Also note. I am using MID360 with inbuilt imu, should i try out an external IMU? will that help?

Some areas its really good

image

some areas its really bad and drifts, its mostly when we go from a large room to a small room or vice versa

image image

also NOTE: FASTLIO2 does not drift in the above case, although it drifts as soon as we enter a small room in some cases, but the above case FASTLIO2 is able to solve.

aditdoshi333 commented 11 months ago

Facing similar issue. Waiting for the author's reply

Lelehel commented 9 months ago

Maybe it's just MID360's poor IMU?

Can you share mid360 config .yaml?

satyajitghana commented 7 months ago

@Lelehel using the one from : https://github.com/hku-mars/FAST_LIO/blob/main/config/mid360.yaml