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 is part of the Self-Driving Cars Specialization
Offered By


About this Course
This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics.
What you will learn
Understand the key methods for parameter and state estimation used for autonomous driving, such as the method of least-squares
Develop a model for typical vehicle localization sensors, including GPS and IMUs
Apply extended and unscented Kalman Filters to a vehicle state estimation problem
Apply LIDAR scan matching and the Iterative Closest Point algorithm
This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics.
Offered by
Syllabus - What you will learn from this course
Module 0: Welcome to Course 2: State Estimation and Localization for Self-Driving Cars
Module 1: Least Squares
Module 2: State Estimation - Linear and Nonlinear Kalman Filters
Module 3: GNSS/INS Sensing for Pose Estimation
Module 4: LIDAR Sensing
Reviews
- 5 stars78.07%
- 4 stars16.02%
- 3 stars3.97%
- 2 stars0.38%
- 1 star1.53%
TOP REVIEWS FROM STATE ESTIMATION AND LOCALIZATION FOR SELF-DRIVING CARS
Challenging but very fun. Not for beginners, you certainly need to know your math and be good enough a coding. Very recommended introduction to state estimation.
A well-taught course by Prof. Jonathan Kelly.I accumulated huge amount of knowledge after undergoing his teachings.The supplementary readings proved to be of great help to ace the final project.
Very good course! I learned how to implement multiple sensor fusion into practice. Thank you!
The course gave a clear and an in-depth knowledge on Kalman filters and Localisation using those filters. The assignments were pretty tough but solving them was fun.
About the Self-Driving Cars Specialization

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