Welcome to this course of events and filtering for sensor fusion, and in this course, we're going
to be covering the theory and a practical implementation for these filtering techniques.
So why focus on common filtering and sense of fusion, data fusion is an amazing tool that is used in
pretty much every piece of modern technology that involves any kind of sensing measurement or automation
or control.
The Common Filter is one of the most widely used methods for data fusion.
By understanding this process, you will be more easily able to understand more complicated methods
later on.
Sensor fusion is one of the key uses of the common filter and is extensively used in unmanned vehicles
and self-driving cars, which are both a very large, evolving fields.
And then lastly, of course, so you don't have to waste your time trying to solve whatever problems
that would be easily avoided with the knowledge inside this course, this course should help you become
a subject matter expert and all things come and filtering.
So this course is for many different people, is for university students or independent learners, is
for aspiring robotics or self-driving cars, engineers or enthusiasts, is for working engineers and
scientists who want to brush up on the knowledge or learn new skill sets, is what engineering professionals
who want to understand the math and skills relating to common filtering and sense of vision is for software
developers who wish to understand the basic concepts behind that diffusion to aid in the implementation
or support of developing data fusion code bases.
And as for anyone who's already proficient with the math in theory and actually want to learn how to
implement the common filters.
So quite a bit is covered in this course.
We're going to be looking at a quick background of basic probability and system theory.
We're going to be talking about the linear common filter.
So the first type of filter that we're going to learn, then we're going to be extending that into nonlinear
systems for the extend the common filter and then for the unscented common filter.
We will also cover some more advanced topics for sense of fusion, such as Falke detection and sensor
error modeling.
We will look at the C++ implementation of a simulation for a self-driving car, in a sense, a fusion
problem.
So fusing different senses that might be aboard a self-driving car and using these types of filters
to come up with the best estimate that represents the state of the system, there are some assumed knowledge
for this course.
So this is a more advanced course than a basic beginner's course.
This course is part of the event series.
So some of the background knowledge in the course is already being assumed knowledge.
And these topics are sort of basic algebra and calculus.
So looking at functions, derivatives integrals is looking at the linear algebra.
So matrix and vector operations is also assume that you have some basic probability or knowledge, basically
squares, estimation, knowledge.
And basic C++ programming knowledge.
So if you know all this, then you'll be in good stead for the course.
If you're lacking in any of these areas, I would recommend brushing up or taking another course to
get you up to speed with these areas.
So by the end of this course, you have another outcomes, you will know how to use the linear comma
filter to solve linear optimal estimation problems.
You know how to use the extended Campfield to solve nonlinear estimation problems.
You also know how to use the uncynical material for the same types of nonlinear estimation problems.
You understand the differences between the different filter types, the advantages and disadvantages
of each.
So when you should use one compared to when you should use the other, you also learn how to use measurements
of Modibo sensors or running at different update rates at the same time so the filter can operate from
Modibo.
Different data sources.
You also learn how to tune the common filter for the best performance.
So this is an important step inside the implementation of a common filter.
It has to be tuned for the problem that you want to solve.
You also know how to correctly initialize the common Philidor for robust operation, which is very important,
you know, how to model sensor errors inside the computer and then correct for them.
You will also know how to use fault detection to remove bad sensor measurements or erroneous measurements.
And you know how to implement the three different common field of variance in C++, and lastly, you'll
know how to implement the linear common filter, the extended filter and unscented common filter in
C++ for an autonomous self-driving car problem, fusing multiple different types of sensors.