MI
5.0Reviewed Aug 11, 2019
Very interesting course if you want to learn about the different filters used in self driving cars for sensor fusion
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: Mathematical Modeling
Status: Estimation
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!!!
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.
GH
5.0Reviewed Apr 28, 2019
one of best experiences. But the course requires a steep learning curve. The discussion forums are really helpful
SI
4.0Reviewed Jul 16, 2020
Thank you for this absolutely fantastic course. Kalman filters and state estimation in general is a concept that I've tried to understand for a long time, and I'm glad to have finally understood it!
JG
5.0Reviewed Apr 18, 2023
Very challenging, nevertheless excelent for learning automation concepts, python programming, sensor fusion, probability & statistics
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.
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.
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.
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!
YC
5.0Reviewed Mar 9, 2019
Could we use C++ to program the projects?And also, in most assignments, please make sure every requirements and additional information are CORRECT and CLEAR! Now, some of them are REALLY MISLEADING!