Odometry and IMU Filtering

[Tweeter0830]

Hey guys,

I'm working on a differential drive rover which I would like to use to follow a path. In order to do this, I plan on using Wheel encoders and a 3DoF IMU. Is there a good way to combine the odometry and IMU measurements to create a more robust estimation of position and orientation?

I plan on modeling my state estimation off of this document.
http://www.seattlerobotics.org/encod...20Anderson.htm

However, he is getting his orientation solely from his IMU and his distance solely from his encoders. Is there a good way to combine the two? I.E. Could I use the acceleration values from my IMU and my encoder measurements to get a more robust estimation of position? How about using my relative wheel speeds and a gyroscope/compass to get a better estimation of orientation? I understand that Kalman and complimentary filters are typically used to combine noisy measurements. However, I have been unable to find an example using a Kalman filter combining IMU and encoder measurements.


Thanks,
-Tweeter

[jwatte]

Give each a confidence level and use statistical methods to improve the guess.
Note that slipping wheels will fool the encoder sensors.

[Th232]

For multiple inputs in a Kalman filter, bear in mind that they need to be the same type of inputs, e.g. you need to get position individually from both sensors, and then combine them in the filter. So...

* Integrate linear and angular IMU readings twice to get position and orientation. Compass reading is just straight orientation. You can use this as a third input to the filter.
* Use the revolutions on each wheel's encoder to find position and orientation.

Now you've got position and orientation from each sensor, stuff the data into the Kalman filter. Tweak gain and so on as needed (as jwatte said, if the wheels slip this will muck things up, so making it rely less on the encoders might be good unless you're sure they won't slip).