So now let's go into the details and framework for the capstone project for the two day vehicle.
So the goal, again, is to estimate the precision, velocity and orientation of a moving vehicle based
on Leida like measurements to known landmarks and features, GPS measurements, gyroscope measurements
and known ideal simulate the conditions.
The problem consists of assuming that the vehicle travels at a constant speed in the 2D.
Explain in the direction it is facing.
Let the inputs be a turn right of the vehicle from a box gyroscope and assume that the acceleration
is a random variable.
So this forms a process model that we want to implement.
In the future.
We will assume that we get precision measurements of the vehicle location, i.e. such as from GPS that
can have sensor faults in them.
So this is going to be the first sensor model.
We are also going to assume that we get a number of range and relative bearing measurements of known
landmark locations, i.e. from a lighted like measurement with and without matching data associations.
So this is the second sensor model that we want to use in inside this field of.
So the presence of the filter is, again, we have a vehicle that has a 2D position, a position X and
Y, it has a velocity and it has a heading.
Now, the velocity is in the direction of heading.
It can be positive or negative or it could be stationary.
We also have a gyroscope on board.
So we get your right information.
We get the side from the gyroscope.
But the gyroscope is also biased.
So we have to now take into account the spies inside the process model and compensate for it.
The acceleration can be model as a random variable with a zero main and a noise variance of Sigma V
squared.
The first since Emoto, the GPS measurements can just be a simple position in the X and Y that is turned
by the sensor.
However, this sensor model is also going to have GPS tonight, areas to areas inside the simulation
environment where we get zero measurements and we can also have a random amount of faulty measurements
inside the GPS measurements.
The second sensor is the lie to lock sensor measurement, so the lighter again produces range and relative
bearing measurements to landmark locations.
However, now we also have to deal with no landmark identification and data association so we can detect
the range and relative bearing to a landmark.
But we might not know which landmark we're actually seeing.
There's no data association with that.
So we're going to have to come up with a process to do this data association for us.
So now let's have a look at the simulation profiles of profiles nine and zero that we're going to be
using for the capstone project.
So in this run here, you can see the common thread is actually running.
We have the uncertainty ellipse and this is the uncertainty ellipse and common field of vision.
If we're just running the unscented common filter that we produced in the last example so we can see
that the vehicle starts at a non-zero initial condition and in fact starts moving in reverse.
So this is going to be the first set of challenges that you encounter when you're using this capstone
project profile once a vehicle starts moving.
We then start to get the Leida measurements and then we enter this orange circle sign here.
So this is when we enter the GPS denied region.
So you can see that we stop getting any GPS measurements inside the circle.
So we're going to be totally relying on the gyroscope and the leider information during this region.
So you can see that the estimated position from the unscented coming forward is moving all over the
place because we don't have valid information.
Once the vehicle leaves the GPS not there, you can see that the GPS measurements come back and then
we go into reverse for a slow period, moving in the reverse direction before the vehicle starts to
accelerate and then starts moving forward.
Towards the end of the profile, we enter back into the GPS to concern before turning back and moving
closer towards to where we initialize the fuel from.
So you can see from the whole profile, the estimated error, the main square there for the position,
the velocity and the heading overall quite large.
The field, it does not perform very well in this situation.
So this is a challenge to try to get the filter to produce as small as estimates as possible over this
profile and the profile without the data association.
Your filter solution should also work for all the other profiles that you can access.
It should be seamlessly working with everything.