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Udemy – Advanced Kalman Filtering and Sensor Fusion 2021-7

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EKF Update Step (Summary)

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Course Content

0 / 74 completed

Section 1: Welcome4 videos

Welcome to the Course

12m

Course Outline

Setting Up C++ Development Environment

Setting Up C++ Simulation

Section 2: Introduction6 videos

What is Sensor Fusion

12m

How Does Sensor Fusion Work

14m

What is the Kalman Filter

Types of Kalman Filters

Learning Roadmap

Simulation Overview

39m

Section 3: Background Theory12 videos

Section Outline

Linear Transformation of Uncertainties

21m

Differential Equations

37m

State Space Representation

10m

Continuous and Discrete Time

Mathematical Models

12m

Discrete Time Conversions

11m

Probability and Estimation

Basic Probability

13m

Probability Density Functions

16m

Multivariate Probability

32m

Gaussian Probability Density Functions

13m

Section 4: Linear Kalman Filter10 videos

How Does the Kalman Filter Work

50m

Kalman Filter Initial Conditions

19m

Simulation Framework

10m

Process Model

13m

Kalman Filter Prediction Step

11m

Kalman Filter Prediction Step Implementation

19m

Kalman Filter Prediction Step Explanation

39m

Kalman Filter Update Step

19m

Kalman Filter Update Step Implementation

12m

Kalman Filter Update Step Explanation

35m

Section 5: Extended Kalman Filter21 videos

What is the Extended Kalman Filter

25m

Lidar Measurement Model

11m

EKF Measurement Innovation (Summary)

13m

EKF Measurement Innovation (Derivation)

17m

EKF Measurement Innovation (Example)

17m

EKF Update Step (Summary)

12mNow Playing

EKF Update Step (Derivation)

14m

EKF Update Step (Example)

EKF 2D Vehicle Filter Update Step

11m

EKF 2D Vehicle Filter Update Step Explanation

26m

Numerical Jacobian Calculation

28m

EKF Simulation Framework

17m

Numerical Jacobian Calculation Example

19m

EKF Understanding and Insights

29m

D Vehicle Process Model

EKF Prediction Step (Summary)

27m

What are Jacobians

16m

EKF Prediction Step (Derivation)

20m

EKF Prediction Step (Example)

EKF 2D Vehicle Filter Prediction Step

21m

EKF 2D Vehicle Filter Prediction Step Explanation

20m

Section 6: Unscented Kalman Filter12 videos

What is the Unscented Kalman Filter

16m

UKF Update Step (Derivation)

12m

UKF 2D Vehicle Filter Update Step

17m

UKF 2D Vehicle Filter Update Step Explanation

Unscented Transformation

26m

UKF Simulation Framework

UKF Prediction Step (Summary)

20m

Matrix Square Root

UKF 2D Vehicle Filter Prediction Step

24m

UKF 2D Vehicle Filter Prediction Step Explanation

19m

UKF Measurement Innovation (Summary)

19m

UKF Update Step (Summary)

11m

Section 7: Filtering in the Real World4 videos

Sensor Models and Errors

19m

Dealing with Faulty Data

22m

Dealing with Sensor Biases

10m

Dealing with Initial Conditions

10m

Section 8: Capstone Project3 videos

Project Overview

Project Details and Framework

19m

Project Hints

Section 9: Conclusion2 videos

Summary

22m

Bonus

About this Lecture

Section: Extended Kalman Filter • Size: 27.09 MB • Duration: 12m

Course Summary

Advanced Kalman Filtering and Sensor Fusion, 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. 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!

Course Content

0 / 74 completed

Section 1: Welcome4 videos

Welcome to the Course

12m

Course Outline

Setting Up C++ Development Environment

Setting Up C++ Simulation

Section 2: Introduction6 videos

What is Sensor Fusion

12m

How Does Sensor Fusion Work

14m

What is the Kalman Filter

Types of Kalman Filters

Learning Roadmap

Simulation Overview

39m

Section 3: Background Theory12 videos

Section Outline

Linear Transformation of Uncertainties

21m

Differential Equations

37m

State Space Representation

10m

Continuous and Discrete Time

Mathematical Models

12m

Discrete Time Conversions

11m

Probability and Estimation

Basic Probability

13m

Probability Density Functions

16m

Multivariate Probability

32m

Gaussian Probability Density Functions

13m

Section 4: Linear Kalman Filter10 videos

How Does the Kalman Filter Work

50m

Kalman Filter Initial Conditions

19m

Simulation Framework

10m

Process Model

13m

Kalman Filter Prediction Step

11m

Kalman Filter Prediction Step Implementation

19m

Kalman Filter Prediction Step Explanation

39m

Kalman Filter Update Step

19m

Kalman Filter Update Step Implementation

12m

Kalman Filter Update Step Explanation

35m

Section 5: Extended Kalman Filter21 videos

What is the Extended Kalman Filter

25m

Lidar Measurement Model

11m

EKF Measurement Innovation (Summary)

13m

EKF Measurement Innovation (Derivation)

17m

EKF Measurement Innovation (Example)

17m

EKF Update Step (Summary)

12mNow Playing

EKF Update Step (Derivation)

14m

EKF Update Step (Example)

EKF 2D Vehicle Filter Update Step

11m

EKF 2D Vehicle Filter Update Step Explanation

26m

Numerical Jacobian Calculation

28m

EKF Simulation Framework

17m

Numerical Jacobian Calculation Example

19m

EKF Understanding and Insights

29m

D Vehicle Process Model

EKF Prediction Step (Summary)

27m

What are Jacobians

16m

EKF Prediction Step (Derivation)

20m

EKF Prediction Step (Example)

EKF 2D Vehicle Filter Prediction Step

21m

EKF 2D Vehicle Filter Prediction Step Explanation

20m

Section 6: Unscented Kalman Filter12 videos

What is the Unscented Kalman Filter

16m

UKF Update Step (Derivation)

12m

UKF 2D Vehicle Filter Update Step

17m

UKF 2D Vehicle Filter Update Step Explanation

Unscented Transformation

26m

UKF Simulation Framework

UKF Prediction Step (Summary)

20m

Matrix Square Root

UKF 2D Vehicle Filter Prediction Step

24m

UKF 2D Vehicle Filter Prediction Step Explanation

19m

UKF Measurement Innovation (Summary)

19m

UKF Update Step (Summary)

11m

Section 7: Filtering in the Real World4 videos

Sensor Models and Errors

19m

Dealing with Faulty Data

22m

Dealing with Sensor Biases

10m

Dealing with Initial Conditions

10m

Section 8: Capstone Project3 videos

Project Overview

Project Details and Framework

19m

Project Hints

Section 9: Conclusion2 videos

Summary

22m

Bonus