Unscented Kalman Filter (Udacity’s Autonomous Car Engineer Nanodegree, term 2, project 2)

In this project, we improved upon the Extended Kalman Filter by using a non-linear model which is better prepared to deal with complex movement patterns. This means the RMSE is going to be lower and our predictions more accurate. It also means the steps are more complex and the added complexity layers hinder a natural understanding of intermediate results. Debugging is not easy!

This table shows the comparison between Unscented using one or two sensors and Extended using two sensors:

Using the Unscented filter required to tune two parameters, the process noises standard deviations of linear and angular acceleration. To guide us in the process we calculated Normalized Innovation Squared values and compared them with the 95% confidence of the corresponding Chi Square distribution. This is the result for the final tuned version of my project:

This video shows the performance of the Unscented Kalman Filter in the same task we used to evaluate the Extended one:

The code for my project is hosted in github:


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