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Welcome to the capstone project of the course.

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So in this project, our goal is to generate the best estimates as you can of the precision, velocity

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and orientation of a moving vehicle based on lifelike measurements to know landmarks and features,

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GPS measurements and gyroscope measurements and real world simulated conditions.

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So this is actually going to be a bit more challenging project that you're going to have to use all

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the skills in the previous projects and all the real world filtering videos to try to solve this problem.

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The challenges that this project is going to throw at you, you have to deal with unknown, non-zero

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initial conditions.

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You also have to deal with sense of biases on the gyroscope.

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We have to deal with faulty or denied sensor measurements from the GPS.

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So such as when you're in the area where you don't have GPS, you get GPS tonight, or the GPS measurements

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might come back with a large amount of error or be faulty.

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We want to be able to deal with complete set of dynamic conditions.

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So this is going forward going in reverse, being stationary.

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We also want to deal with landmark data association problems.

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So so Nevada gives you range and bearing measurements to landmarks, but it might actually not tell

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you what landmark it is looking at is not giving you the data association.

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And this is fairly common for Leida.

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It only gives you range and bearing to some point in the environment.

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And we have to do all this while providing the best overall estimation performance.

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So these are the challenges for the capstone project that you're going to have to complete.

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So you can sort of think of this as a bit of a competition.

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If you take up this challenge, post your results with profiles nine and zero.

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So these are the capstone project profiles in the common Q&A question section to compete with other

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people and compare the different results that you get.