Alicia White

You feel acceleration, you know, the outward push of you as you're spinning around. An inertial measurement unit can use both of these sensors and know which one is wrong. And it's kind of like your ears and your eyes. If you are spinning around in a circle and your eyes are closed, you're going to get dizzy much faster than if your eyes are open.

You feel acceleration, you know, the outward push of you as you're spinning around. An inertial measurement unit can use both of these sensors and know which one is wrong. And it's kind of like your ears and your eyes. If you are spinning around in a circle and your eyes are closed, you're going to get dizzy much faster than if your eyes are open.

The Kalman filter is a way to combine sensors with different error features, different error characteristics into a way to get a much better response because you can characterize the error when nothing's happening. Or you can characterize the error in certain conditions where you know one is good and one is bad, like on a clover leaf.

The Kalman filter is a way to combine sensors with different error features, different error characteristics into a way to get a much better response because you can characterize the error when nothing's happening. Or you can characterize the error in certain conditions where you know one is good and one is bad, like on a clover leaf.

They would provide the algorithm, yeah. And they would provide the base code for how good each of their sensors are.

They would provide the algorithm, yeah. And they would provide the base code for how good each of their sensors are.

Exactly.

Exactly.

Better than I want to.

Better than I want to.

In 1990, it was a patent I worked on.

In 1990, it was a patent I worked on.

Well, no, I helped implement them.

Well, no, I helped implement them.

It came out of a, and to be fair, a lot of people started doing IMUs and Kalman filters in the early to mid 90s. And it exploded as to the applications, but a bunch of PhDs were done at that time. And so they became very, very popular because they solve the inertial measurement unit problem pretty well. And now they solve a lot of other problems that involve a bunch of sensors.

It came out of a, and to be fair, a lot of people started doing IMUs and Kalman filters in the early to mid 90s. And it exploded as to the applications, but a bunch of PhDs were done at that time. And so they became very, very popular because they solve the inertial measurement unit problem pretty well. And now they solve a lot of other problems that involve a bunch of sensors.

Usually when we talk about self-driving cars, the GPS has one set of errors, mostly it's really slow. The LiDAR or camera can have another set of errors and it provides a different set of information and the different types of Kalmans can put all of these together. And the idea is just you put the errors here and you put the data here and you try to make the best out of the bad situation you have.

Usually when we talk about self-driving cars, the GPS has one set of errors, mostly it's really slow. The LiDAR or camera can have another set of errors and it provides a different set of information and the different types of Kalmans can put all of these together. And the idea is just you put the errors here and you put the data here and you try to make the best out of the bad situation you have.

I can't picture doing it only with cameras.

I can't picture doing it only with cameras.