Descriptions
Advanced Kalman Filtering and Sensor Fusion. You need to know about sensor fusion and Kalman filtering! In this course, learn how to use these concepts and implement them with a focus on autonomous vehicles. 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 of the 20th century. It has enabled humanity to do and build many things that would not otherwise be possible. It has immediate applications in controlling complex dynamic systems such as cars, airplanes, ships, and spacecraft. These concepts are used detailed They are used in many fields, such as engineering and manufacturing, but are also used in many other areas such as chemistry, biology, finance, economics, etc. You’ll learn the theory from the ground up so you can fully understand how it works and what impact things have on the end result. You’ll also learn the practical implementation of the techniques so you know how to put theory into practice. In this course, you’ll work with a C++ simulation that will walk you through implementing various Kalman filter 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!
What you will learn
- Using the linear Kalman filter to solve linear optimal estimation problems
- Using the extended Kalman filter to solve nonlinear estimation problems
- How to use the Unscented Kalman Filter to solve nonlinear estimation problems
- How to merge measurements from multiple sensors that all run at different update rates
- How to optimize the Kalman filter for best performance
- How to correctly initialize the Kalman filter for robust operation
- How to model sensor errors in the Kalman filter
- How to use error detection to remove erroneous sensor measurements
- How to implement the above 3 Kalman filter variants in C++
- How to implement LKF in C++ for a 2D tracking problem
- How to implement EKF and UKF in C++ for an autonomous self-driving car problem
Who is this course suitable for?
- University students or independent learners
- Budding robot or self-driving car engineers
- Professional engineers and scientists
- Engineers who want to refresh their mathematical theories and skills related to Kalman filtering and sensor fusion
- Software developers who want to understand the basic concepts of data fusion to help implement or support the development of data fusion code.
- Anyone who already knows mathematics “in theory” and wants to learn how to translate the theory into code.
Specifications of extended Kalman filtering and sensor fusion
- Editor: Udemy
- Teacher: Steven Dumble
- Language: English
- Level: Intermediate
- Number of courses: 82
- Duration: 8 hours and 20 minutes
Contents of Advanced Kalman Filtering and Sensor Fusion
Requirements
- A curious mind!
- Basic arithmetic: functions, derivatives, integrals
- Linear Algebra: Matrix and Vector Operations
- Basic Probability
- Basic C++ programming knowledge
Pictures
Sample clip
installation Guide
Extract the files and watch them with your favorite player
Subtitles: English
Quality: 720p
Download links
Password file(s): free download software
File size
2.15GB