31 estimator ekf refactor #68
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The propagate model and measurment updates have been put in a callable format. There is also error checking for the inputs. UNTESTED
Some functions were needed to update on each call so we have to pass the functions. Checks will largely be made at compile time which will remove the benefit of our checks so I removed them.
Fixed the definitions to make more sense for the propagate and measurment updates.
implementation. Tested and it works!
The attitude estimation are now happening using the ekf, it has been tested and it works as well as the previous.
Showed what each line is doing, in an effort to make it clear what to do if you want to add a sensor.
This actually has a marginal increase in position accuracy. Mainly it allows you to only define 2 functions and get your estimate. It also does not have the same issue where the course estimate is bad if you use a very slightly older estimate.
It would be nice to have a more clean abstraction that binds the funcitons. I think that would improve readablity. I tried this but it was too complex, and not worth it. Instead I put them all in a function at the bottom with an explanation.
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bsutherland333
approved these changes
Jul 12, 2024
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The refactor is complete! There is now an a class that has all of the tools to make an EKF. It has a few functions to make this easier, specifically:
propagate_model' - Propagates model for the prediction step.measurement_update- Updates the state estimate based on the predicted measure and the actual measure.single_measurement_update` - If it is preferred you can update one measurement at a time.I tested both of the methods of one measure at a time or all at once. Both are functional there was a minor improvement for the all-at-once.
There will be a lot of conflicts since the renaming happened.
What are y'all's thoughts on the implementation?