Mô tả

You need to learn know Sensor Fusion and Kalman Filtering! Learn how to use these concepts and implement them with a focus on autonomous vehicles in this course.

The Kalman filter is one of the greatest discoveries in the history of estimation and data fusion theory, and perhaps one of the greatest engineering discoveries in the twentieth century. It has enabled mankind to do and build many things which could not be possible otherwise. It has immediate application in control of complex dynamic systems such as cars, aircraft, ships and spacecraft.

These concepts are used extensively in engineering and manufacturing but they are also used in many other areas such as chemistry, biology, finance, economics, and so on.

Why focus on Sensor Fusion and Kalman Filtering

  • Data Fusion is an amazing tool that is used pretty much in every modern piece of technology that involves any kind of sensing, measurement or automation.

  • The Kalman Filter is one of the most widely used methods for data fusion. By understanding this process you will more easily understand more complicated methods.

  • Sensor fusion is one of the key uses of Kalman Filtering and is extensively used in unmanned vehicles and self-driving cars.

  • Evaluating and tuning the Kalman Filter for best performance can be a bit of a 'black art', we will give you tips and a structure so you know how to do this yourself.

  • So you don’t waste time trying to solve or debug problems that would be easily avoided with this knowledge! Become a Subject Matter Expert!

What you will learn:

You will learn the theory from ground up, so you can completely understand how it works and the implications things have on the end result. You will also learn practical implementation of the techniques, so you know how to put the theory into practice. In this course you will work with a C++ simulation that leads you through the implementation of various Kalman filtering methods for autonomous vehicles.

At the end of the course, the Capstone project is to implement the Unscented Kalman Filter and run it as it would be used in a real self-driving car or autonomous vehicle!

We will cover:

  • Basic Background Probability and Systems Theory

  • Linear Kalman Filtering

  • Extended Kalman Filtering

  • Unscented Kalman Filtering

  • Advanced Topics for Sensor Fusion, such as fault detection and sensor error modelling.

  • C++ Implementation in simulation for a self-driving car sensor fusion problem.

By the end of this course you will know:

  • How to use the Linear Kalman Filter to solve linear optimal estimation problems

  • How to use the Extended Kalman Filter to solve non-linear estimation problems

  • How to use the Unscented Kalman Filter to solve non-linear estimation problems

  • How to fuse in measurements of multiple sensors all running at different update rates

  • How to tune the Kalman Filter for best performance

  • How to correctly initialize the Kalman Filter for robust operation

  • How to model sensor errors inside the Kalman Filter

  • How to use fault detection to remove Bad Sensor measurements

  • How to implement the above 3 Kalman Filter Variants in C++

  • How to implement the LKF in C++ for a 2d Tracking Problem

  • How to implement the EKF and UKF in C++ for an autonomous self-driving car problem

What are the course requirements or prerequisites:

This course is part of the more advanced series and as such it does have a few prerequisites:

  • Basic Calculus: Functions, Derivatives, Integrals

  • Linear Algebra: Matrix and Vector Operations

  • Basic Probability

  • Basic C++ Programming Knowledge

Who is this course for:

  • University students or independent learners.

  • Aspiring robotic or self-driving car engineers or enthusiasts.

  • Working Engineers and Scientists.

  • Engineering professionals who want to brush up on the math theory and skills related to Kalman filtering and Sensor Fusion.

  • Software Developers who wish to understand the basic concepts behind data fusion to aid in implementation or support of developing data fusion code.

  • Anyone already proficient with the math “in theory” and want to learn how to implement the theory in code.

What you will get in this course:

  • >8 hours of video lectures that include explanations and walk thoughts, pictures, diagrams and animations.

  • PDF documents of cheat sheets with important notes and exercises

  • C++ simulation code for a self driving car example.

  • All the source code and friendly support in the Q&A area.

Why am I qualified to teach this course:

I have been employed for the last decade as a Guidance, Navigation and Control engineer for a number of aerospace and automation companies, focusing on sensor fusion for aircraft, missile and vehicle state estimation. I have taught this content to bachelor’s, master’s and PhD students while teaching at university and to engineering professionals.

So what are you waiting for??

Watch the course instruction video and free samples so that you can get an idea of what the course is like. If you think this course will help you then sign up, money back guarantee if this course is not right for you.

I hope to see you soon in the course!

Steve

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9 sections

Welcome

6 lectures
Welcome to the Course
04:58
Course Outline
01:43
Setting Up C++ Development Environment
02:27
Setting Up C++ Simulation
01:26
C++ Simulation Readme
03:01
Course Resources
00:15

Introduction

9 lectures
What is Sensor Fusion
04:10
How Does Sensor Fusion Work
05:07
How Does Sensor Fusion Work Notes
01:45
What is the Kalman Filter
03:53
What is the Kalman Filter Notes
01:14
Types of Kalman Filters
02:32
Types of Kalman Filters Notes
00:32
Learning Roadmap
01:56
Simulation Overview
09:02

Background Theory

17 lectures
Section Outline
01:09
Basic Probability
07:18
Basic Probability Quiz
7 questions
Probability Density Functions
09:29
Probability Density Functions Quiz
5 questions
Multivariate Probability
16:47
Multivariate Probability Quiz
4 questions
Gaussian Probability Density Functions
07:31
Gaussian Probability Density Functions Quiz
3 questions
Linear Transformation of Uncertainties
09:49
Linear Transformation of Uncertainties Quiz
2 questions
Differential Equations
03:54
State Space Representation
04:28
Continuous and Discrete Time
03:51
Mathematical Models
06:45
Discrete Time Conversions
07:21
Probability and Estimation
04:06

Linear Kalman Filter

11 lectures
How Does the Kalman Filter Work
07:10
Simulation Framework
04:39
Process Model
06:40
Kalman Filter Prediction Step
05:01
Kalman Filter Prediction Step Implementation
08:22
Kalman Filter Prediction Step Explanation
11:22
Kalman Filter Update Step
08:21
Kalman Filter Update Step Implementation
05:22
Kalman Filter Update Step Explanation
09:29
Kalman Filter Initial Conditions
06:33
Kalman Filter Summary
Processing..

Extended Kalman Filter

22 lectures
What is the Extended Kalman Filter
12:38
EKF Simulation Framework
06:54
2D Vehicle Process Model
04:03
EKF Prediction Step (Summary)
13:10
What are Jacobians
06:42
EKF Prediction Step (Derivation)
08:59
EKF Prediction Step (Example)
04:21
EKF 2D Vehicle Filter Prediction Step
08:00
EKF 2D Vehicle Filter Prediction Step Explanation
04:42
Lidar Measurement Model
05:07
EKF Measurement Innovation (Summary)
06:34
EKF Measurement Innovation (Derivation)
08:12
EKF Measurement Innovation (Example)
08:26
EKF Update Step (Summary)
05:05
EKF Update Step (Derivation)
06:51
EKF Update Step (Example)
03:53
EKF 2D Vehicle Filter Update Step
04:31
EKF 2D Vehicle Filter Update Step Explanation
07:29
Numerical Jacobian Calculation
13:34
Numerical Jacobian Calculation Example
09:01
EKF Understanding and Insights
15:53
Extended Kalman Filter Summary
Processing..

Unscented Kalman Filter

13 lectures
What is the Unscented Kalman Filter
06:22
Unscented Transformation
14:24
UKF Simulation Framework
03:20
UKF Prediction Step (Summary)
11:37
Matrix Square Root
03:41
UKF 2D Vehicle Filter Prediction Step
09:03
UKF 2D Vehicle Filter Prediction Step Explanation
06:11
UKF Measurement Innovation (Summary)
09:22
UKF Update Step (Summary)
05:23
UKF Update Step (Derivation)
06:20
UKF 2D Vehicle Filter Update Step
06:56
UKF 2D Vehicle Filter Update Step Explanation
02:27
Unscented Kalman Filter Summary
Processing..

Filtering in the Real World

4 lectures
Sensor Models and Errors
11:13
Dealing with Faulty Data
11:02
Dealing with Sensor Biases
04:24
Dealing with Initial Conditions
04:23

Capstone Project

3 lectures
Project Overview
02:06
Project Details and Framework
04:47
Project Hints
03:58

Conclusion

2 lectures
Summary
09:04
Bonus
00:24

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