Advanced Kalman Filtering and Sensor Fusion from Udemy

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

What's inside

Learning objectives

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
Show more
Show less

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
Show more
Show less

Syllabus

Welcome

Welcome to the Course

Course Outline

Setting Up C++ Development Environment

Setting Up C++ Simulation

C++ Simulation Readme

Course Resources

Introduction

What is Sensor Fusion

How Does Sensor Fusion Work

How Does Sensor Fusion Work Notes

What is the Kalman Filter

What is the Kalman Filter Notes

Types of Kalman Filters

Types of Kalman Filters Notes

Learning Roadmap

Simulation Overview

Background Theory

Section Outline

Basic Probability

Basic Probability Quiz

Probability Density Functions

Probability Density Functions Quiz

Multivariate Probability

Multivariate Probability Quiz

Gaussian Probability Density Functions

Gaussian Probability Density Functions Quiz

Linear Transformation of Uncertainties

Linear Transformation of Uncertainties Quiz

Differential Equations

State Space Representation

Continuous and Discrete Time

Mathematical Models

Discrete Time Conversions

Probability and Estimation

Linear Kalman Filter

How Does the Kalman Filter Work

Simulation Framework

Process Model

Kalman Filter Prediction Step

Kalman Filter Prediction Step Implementation

Kalman Filter Prediction Step Explanation

Kalman Filter Update Step

Kalman Filter Update Step Implementation

Kalman Filter Update Step Explanation

Kalman Filter Initial Conditions

Kalman Filter Summary

Extended Kalman Filter

What is the Extended Kalman Filter

EKF Simulation Framework

2D Vehicle Process Model

EKF Prediction Step (Summary)

What are Jacobians

EKF Prediction Step (Derivation)

EKF Prediction Step (Example)

EKF 2D Vehicle Filter Prediction Step

EKF 2D Vehicle Filter Prediction Step Explanation

Lidar Measurement Model

EKF Measurement Innovation (Summary)

EKF Measurement Innovation (Derivation)

EKF Measurement Innovation (Example)

EKF Update Step (Summary)

EKF Update Step (Derivation)

EKF Update Step (Example)

EKF 2D Vehicle Filter Update Step

EKF 2D Vehicle Filter Update Step Explanation

Numerical Jacobian Calculation

Numerical Jacobian Calculation Example

EKF Understanding and Insights

Extended Kalman Filter Summary

Unscented Kalman Filter

What is the Unscented Kalman Filter

Unscented Transformation

UKF Simulation Framework

UKF Prediction Step (Summary)

Matrix Square Root

UKF 2D Vehicle Filter Prediction Step

UKF 2D Vehicle Filter Prediction Step Explanation

UKF Measurement Innovation (Summary)

UKF Update Step (Summary)

UKF Update Step (Derivation)

UKF 2D Vehicle Filter Update Step

UKF 2D Vehicle Filter Update Step Explanation

Unscented Kalman Filter Summary

Filtering in the Real World

Sensor Models and Errors

Dealing with Faulty Data

Dealing with Sensor Biases

Dealing with Initial Conditions

Capstone Project

Project Overview

Project Details and Framework

Project Hints

Conclusion

Summary

Bonus

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches skills used extensively in engineering and manufacturing, which are in high demand in industry

Taught by an engineer who has worked in the field for over a decade, so you can be sure you're learning from an expert

Uses C++ for implementation, which is a valuable skill for engineers and developers

Covers advanced topics like sensor error modeling and fault detection, which are essential for building robust self-driving systems

Provides a comprehensive study of sensor fusion and Kalman filtering, which are key technologies in autonomous vehicles

Aimed at university students or independent learners, so it's suitable for a wide range of audiences

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Advanced Kalman Filtering and Sensor Fusion with these activities:

Read Robot Modeling and Control

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This book provides a comprehensive overview of robot modeling and control. It will help you to understand the fundamentals of robotics, including sensor fusion and the Kalman Filter.

View Robot Modeling and Control on Amazon

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Read Chapter 10
Do Chapter 10 exercises

Practice linear algebra and differential equations

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This will help you to refresh your math skills, which are essential for understanding sensor fusion and the Kalman Filter.

Browse courses on Linear Algebra

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Review the notes or textbook on the topic
Do 10 practice problems

Solve Probability and Calculus exercises

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Practicing these skills outside the context of the course will help solidify your understanding.

Browse courses on Probability

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Review the notes or textbook on the topic
Do 10 practice problems

Six other activities

Expand to see all activities and additional details

Show all nine activities

Follow a tutorial on sensor fusion

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This will help you get a better understanding of the concepts and how they are applied.

Browse courses on Sensor Fusion

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Find a tutorial on sensor fusion
Follow the tutorial

Solve Kalman Filter Exercises

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Practice solving Kalman Filter exercises to reinforce theoretical concepts.

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Find online resources or textbooks with Kalman Filter exercises.
Attempt to solve the exercises independently.
Compare your solutions with provided answers or discuss with peers.

Follow Tutorials on Unscented Kalman Filter (UKF)

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Explore tutorials to gain a deeper understanding of the Unscented Kalman Filter and its applications.

Browse courses on Unscented Kalman Filter

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Identify relevant tutorials on Unscented Kalman Filters.
Follow the tutorials step-by-step, implementing the concepts in Python
Experiment with different parameters to observe the impact on filter performance.

Write a summary of the Kalman Filter

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This will help you solidify your understanding of the Kalman Filter.

Browse courses on Kalman Filter

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Read the course materials on the Kalman Filter
Write a summary of the Kalman Filter

Implement a Kalman Filter in Python

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Create a project to build a practical understanding of Kalman Filter implementation.

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Implement a Kalman Filter in C++

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This will help you apply your understanding of the Kalman Filter to a real-world problem.

Browse courses on Kalman Filter

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Find a dataset that you can use to test your Kalman Filter
Implement a Kalman Filter in C++
Test your Kalman Filter using the dataset

Career center

Learners who complete Advanced Kalman Filtering and Sensor Fusion will develop knowledge and skills that may be useful to these careers:

Navigation Engineer

Navigation Engineers design, develop, and test systems that help vehicles navigate their environment. They use their knowledge of GPS, inertial navigation, and sensor fusion to ensure that vehicles are able to determine their location and orientation accurately. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing autonomous vehicles.

See salaries and explore the career path for Navigation Engineer

Vehicle Dynamics Engineer

Vehicle Dynamics Engineers design, develop, and test systems that control the dynamics of vehicles. They use their knowledge of mechanics, dynamics, and control theory to ensure that vehicles are able to handle and perform safely. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing autonomous vehicles.

See salaries and explore the career path for Vehicle Dynamics Engineer

Guidance, Navigation, and Control Engineer

Guidance, Navigation, and Control Engineers design, develop, and test systems that control the movement of vehicles. They use their knowledge of kinematics, dynamics, and control theory to ensure that vehicles are able to move safely and efficiently. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing autonomous vehicles.

See salaries and explore the career path for Guidance, Navigation, and Control Engineer

Robotics Engineer

Robotics Engineers design, develop, and test robots. They use their knowledge of mechanics, electronics, and computer science to develop robots that can perform a variety of tasks. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing autonomous robots.

See salaries and explore the career path for Robotics Engineer

Machine Learning Engineer

Machine learning engineers design, develop, and test machine learning models. They use their knowledge of machine learning algorithms and data analysis to develop models that can learn from data and make predictions. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing machine learning models for autonomous vehicles.

See salaries and explore the career path for Machine Learning Engineer

Systems Engineer

Systems Engineers design, develop, and test complex systems. They use their knowledge of systems engineering principles to ensure that systems are able to meet their requirements. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing autonomous vehicles.

See salaries and explore the career path for Systems Engineer

Transportation Engineer

Transportation Engineers design, develop, and test transportation systems. They use their knowledge of traffic engineering, transportation planning, and transportation safety to ensure that transportation systems are able to meet the needs of users. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing autonomous vehicles.

See salaries and explore the career path for Transportation Engineer

Automotive Engineer

Automotive engineers design, develop, and test vehicles and their components. They use their knowledge of engineering principles to improve the performance, safety, and efficiency of vehicles. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing autonomous driving systems.

See salaries and explore the career path for Automotive Engineer

Aerospace Engineer

Aerospace Engineers design, test, and oversee the production of aircraft, spacecraft, and missiles. They apply advanced mathematical and scientific principles to ensure the safety and efficiency of air and space vehicles. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing guidance, navigation, and control systems for autonomous vehicles.

See salaries and explore the career path for Aerospace Engineer

Test Engineer

Test Engineers design, develop, and test products to ensure that they meet their requirements. They use their knowledge of testing principles and techniques to develop tests that can identify defects and ensure that products are safe and reliable. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing autonomous vehicles.

See salaries and explore the career path for Test Engineer

Mechanical Engineer

Mechanical Engineers design, develop, and test mechanical systems and components. They use their knowledge of mechanics, materials, and thermodynamics to ensure the safety and efficiency of mechanical systems. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing autonomous vehicles.

See salaries and explore the career path for Mechanical Engineer

Software Engineer

Software Engineers design, develop, and test software applications. They use their knowledge of computer science and software engineering principles to develop software that meets the needs of users. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing software for autonomous vehicles.

See salaries and explore the career path for Software Engineer

Control Systems Engineer

Control Systems Engineers design, develop, and test systems that control the behavior of machines and processes. They use their knowledge of feedback control theory to ensure that systems are stable, efficient, and responsive. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing control systems for autonomous vehicles.

See salaries and explore the career path for Control Systems Engineer

Electrical Engineer

Electrical Engineers design, develop, and test electrical systems and components. They use their knowledge of electricity, electronics, and magnetism to ensure the safety and efficiency of electrical systems. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing autonomous vehicles.

See salaries and explore the career path for Electrical Engineer

Data Scientist

Data scientists use their knowledge of statistics, machine learning, and data analysis to extract insights from data. They work in a variety of industries, including finance, healthcare, and manufacturing. This course can help build a foundation in sensor fusion and Kalman filtering, which are essential for developing data fusion algorithms.

See salaries and explore the career path for Data Scientist