State Estimation and Localization for Self-Driving Cars

Welcome to State Estimation and Localization for Self-Driving Cars, the second course in University of Toronto’s Self-Driving Cars Specialization. We recommend you take the first course in the Specialization prior to taking this course. This course will introduce you to the different sensors and how we can use them for state estimation and localization in a self-driving car. By the end of this course, you will be able to: - Understand the key methods for parameter and state estimation used for

• Understand parameter and state estimation methods like least-squares
• Develop sensor models for GPS and IMUs
• Apply extended and unscented Kalman Filters for state estimation
• Use LIDAR scan matching and Iterative Closest Point algorithm