Back to State Estimation and Localization for Self-Driving Cars
University of Toronto

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 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 - Understand LIDAR scan matching and the Iterative Closest Point algorithm - Apply these tools to fuse multiple sensor streams into a single state estimate for a self-driving car For the final project in this course, you will implement the Error-State Extended Kalman Filter (ES-EKF) to localize a vehicle using data from the CARLA simulator. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws).

Status: Machine Learning Methods
Status: Applied Mathematics
AdvancedCourse27 hours

Featured reviews

MP

5.0Reviewed Jun 12, 2020

A lot of fun! I learnt a lot and feel that due to the well designed assignments I really got to the bottom of it...

TM

5.0Reviewed Jun 11, 2024

This is an eye-opening course on how to utilize statistical analysis for engineering applications, and in particular, autonomous systems, as such it is very useful and captivating course!!!

SS

4.0Reviewed Aug 12, 2019

For new learners, this course provides the beginner to intermediate knowledge. The explanation with examples are quite interesting and easy.

EJ

5.0Reviewed Dec 13, 2021

I have learned KF in the past. First time learning EKF. I liked the rigor in this course! Felt like a legitimate university lesson.

HK

5.0Reviewed May 22, 2021

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.

WS

5.0Reviewed Oct 13, 2019

There are many interesting topics. Without the help and suggested readings from this course, I wouldn't be able to finish by myself. Also, the final project is very enlightening.

JG

5.0Reviewed Apr 18, 2023

Very challenging, nevertheless excelent for learning automation concepts, python programming, sensor fusion, probability & statistics

DM

5.0Reviewed Jun 21, 2025

The course is highly informative and offers excellent opportunities to gain practical, hands-on skills essential for real-world autonomous vehicle applications.

H

4.0Reviewed May 21, 2020

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.

RL

5.0Reviewed Apr 26, 2019

It provides a hand-on experience in implementing part of the localization process...interesting stuff!! Kind of time-consuming so be prepared.

MM

5.0Reviewed May 21, 2020

A great Journey for anyone interested in Self Driving Cars. State estimation is vital in this field and this is a great course to start learning it!

CA

5.0Reviewed Mar 19, 2021

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.

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