Welcome to the capstone project of the course.
So in this project, our goal is to generate the best estimates as you can of the precision, velocity
and orientation of a moving vehicle based on lifelike measurements to know landmarks and features,
GPS measurements and gyroscope measurements and real world simulated conditions.
So this is actually going to be a bit more challenging project that you're going to have to use all
the skills in the previous projects and all the real world filtering videos to try to solve this problem.
The challenges that this project is going to throw at you, you have to deal with unknown, non-zero
initial conditions.
You also have to deal with sense of biases on the gyroscope.
We have to deal with faulty or denied sensor measurements from the GPS.
So such as when you're in the area where you don't have GPS, you get GPS tonight, or the GPS measurements
might come back with a large amount of error or be faulty.
We want to be able to deal with complete set of dynamic conditions.
So this is going forward going in reverse, being stationary.
We also want to deal with landmark data association problems.
So so Nevada gives you range and bearing measurements to landmarks, but it might actually not tell
you what landmark it is looking at is not giving you the data association.
And this is fairly common for Leida.
It only gives you range and bearing to some point in the environment.
And we have to do all this while providing the best overall estimation performance.
So these are the challenges for the capstone project that you're going to have to complete.
So you can sort of think of this as a bit of a competition.
If you take up this challenge, post your results with profiles nine and zero.
So these are the capstone project profiles in the common Q&A question section to compete with other
people and compare the different results that you get.