mini-pyllio

A very minimal but working Python only implementation for loosely-coupled LiDAR-Inertial odometry for education purpose.

What's this?

Recently, Pypose made a great example code for the imu integration with an easy-to-follow python-based code. see https://github.com/pypose/pypose/tree/main/examples/module/imu. This is called inertial odometry.
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.
  1. A LiDAR prevents an IMU diverges.
  2. 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.

A set of additional efforts could enhance the speed/accruacy.
- For example,
  1. I used scan-to-scan matching. Scan-to-submap matching would definitely helpful.
  2. Not explicit replacement of the state, but trying weighting the propagated state and the correction such as Kalman filter.
  3. Fusion with a cheap GPS.
But, this is a minimal codebase for an educational purpose, thus I left them for you. Do it yourself!