Advanced Kalman Filtering and Sensor Fusion
Theory and C++ Simulation Implementation for Autonomous Vehicles and Self Driving Cars!
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.
Best Seller Course: Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs
What you’ll learn
- 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
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