Advanced Kalman Filtering and Sensor Fusion

Unlock the power of Kalman filtering and sensor fusion with this free course! Learn how to apply these concepts to real-world applications, particularly in the field of autonomous vehicles. This comprehensive course, taught by experienced engineer Steve, covers the theory and implementation of Kalman filtering, taking you from basic principles to advanced techniques like the Unscented Kalman Filter.

This course, available for free on Theetay, offers access to high-quality content from platforms like Udemy, Udacity, Coursera, MasterClass, NearPeer, and more. Dive into the world of data fusion and explore the use of Kalman filtering in autonomous vehicles, robotics, and other industries.

Key Topics Covered:

• Basic Probability and Systems Theory
• Linear Kalman Filtering
• Extended Kalman Filtering
• Unscented Kalman Filtering
• Advanced Sensor Fusion Techniques
• C++ Implementation for Self-Driving Car Sensor Fusion

What You’ll Learn:

• Implement the Linear Kalman Filter for linear estimation problems.
• Apply the Extended Kalman Filter for non-linear estimation problems.
• Master the Unscented Kalman Filter for solving non-linear estimation problems.
• Fuse measurements from multiple sensors at different update rates.
• Optimize Kalman Filter performance through tuning.
• Initialize the Kalman Filter effectively for robust operation.
• Model sensor errors within the Kalman Filter framework.
• Implement fault detection to eliminate faulty sensor measurements.
• Develop C++ code for various Kalman Filter variants.
• Implement the Linear Kalman Filter in C++ for 2D tracking.
• Implement the Extended and Unscented Kalman Filters in C++ for autonomous vehicle applications.

Prerequisites:

• Basic Calculus
• Linear Algebra
• Basic Probability
• Basic C++ Programming Knowledge

Who This Course Is For:

• University students and independent learners
• Aspiring robotic or self-driving car engineers and enthusiasts
• Working engineers and scientists
• Engineering professionals looking to enhance their knowledge of Kalman filtering and sensor fusion
• Software developers interested in data fusion concepts and implementation
• Individuals with a theoretical understanding of mathematics seeking practical implementation skills

Course Benefits:

• Over 8 hours of video lectures with detailed explanations, diagrams, and animations
• PDF documents containing cheat sheets, important notes, and exercises
• C++ simulation code for a self-driving car example
• Access to all source code and support through the Q&A forum

Start your journey into the world of Kalman filtering and sensor fusion today! Enroll in this free course on Theetay and gain the knowledge and skills to excel in cutting-edge technologies.