# No difference between UKF and EKF for SLAM

I built EKF and UKF SLAM algorithms. The problem is that I expected to see a difference because of the more precise approximation of the system in the UKF.

Here's a screenshot from the estimated path from both filters [sorry about the German]:

As you can see, the differences are very minor and sum up to an equal performance of both filters within the mean errors for every estimated state. I thought increasing the system noise for a bigger uncertainty would make the advantages of the UKF clearer but it didn't make a difference.

UKF Parameters: $$\alpha$$: 0.01, $$\kappa$$: 0, $$\beta$$: 0.

My question is, what could be the reason for the similiar results of both filters and how can we enhence the UKF performance?

Thanks.

Nvm, the result improved with UKF parameters from a backup. The parameters where to low. New Parameters: $$\alpha$$: 0.5, $$\kappa$$: 25, $$\beta$$: 2.

New Result

Mean error X-Position

EKF 0.7572

UKF 0.2501

mean error Y-Position

EKF 0.4535

UKF 0.1708

Mean error orientation

EKF 0.0112

UKF 0.0083