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Learner Reviews & Feedback for State Estimation and Localization for Self-Driving Cars by University of Toronto

779 ratings

About the Course

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)....

Top reviews


Oct 29, 2019

best online course so far that explains kalman filter and estimation methods with examples not just focusing on theoretical ,Thanks to the Dr's and course staff who worked hard to produce this course.


Feb 9, 2021

The course is informative and well constructed for learners. The final project is designed well so that we can build sensor fusion tools while applying what we have learned from this course.

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1 - 25 of 125 Reviews for State Estimation and Localization for Self-Driving Cars

By Jon H

Jun 4, 2019

There is no support for this class

The forums are almost useless and no teacher or staff ever answers anything on them

The lectures are pure fluff and hand-waving, no meat and no details

The projects are extremely difficult and there is no lessons to cover material needed for the projects

Would not recommend unless you want to basically learn on your own

Too much work BTW I did get 100%.

By MachWave

Jul 1, 2019

Review :

Mentor Help: 0/5

Course Content: 4/5

Course Explanation: 4/5

Course Challenging: 4/5

Exercises : 3/5

Things which can be improved: There should be a programming exercise for each module especially for modules like ICP. There should be more mentor support as everything can't be understood by videos. There is/was an expectation of doing the final project in CARLA online but it was offline and also the ICP was pre-implemented. But overall for starters it is a very good course for state estimation to support and I strongly suggest to complete it if you aspire to be a self - driving car engineer.

By Rade

Jun 7, 2019

Very dry lectures!

Quiz automated grader buggy and not working at times. Example: not well defined python environment for the quiz in module 4. A grader expects a certain format that you have to guess. But to guess you need to submit the quiz in order to see if you satisfied the grader. So you can do that 5 times every our. A lot of time spent on satisfying the grader format that learning material.

The reason I am realty trying to stay in the class is because I am very interested in the subject but the execution of this class is a disaster!

By Asad Q

Feb 9, 2020

One of the most exciting courses ever had in terms of learning and understanding. Kalman filter is a fascinating concept with infinite applications in real life on daily basis.

By Wit S

Oct 14, 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.

By Guruprasad M H

Apr 29, 2019

one of best experiences. But the course requires a steep learning curve. The discussion forums are really helpful

By Remon G

Aug 12, 2019

Very useful!

Great experience!

Congratulation all the people involved in this course!

By River L

Apr 27, 2019

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

By Joachim S

Jun 11, 2019

I was impressed about the different methods available to do state estimation. The content was well presented (all slides shown are available as a PDF download) although in a quite compressed fashion. As in course 1 I would have preferred much longer videos so that more details of the different models could have been highlighted. Personally I was amazed about concepts like the Quarternion that I have never heart about before. A great plus from my perspective is that - like in course 1 - every lesson has a list of further articles to read - and in order to really comprehend the stuff presented I recommend in doing a deep-dive into these articles. Personally I found the coding assignments really demanding and as a side note I would have appreciated a little bit more presence of the teaching stuff to clarify. Currently the impression is that besides a monthly post in the discussion forum the teaching stuff is not visible - which is really sad as I think this whole specialization to be prime content. Unfortunately the locked video that will be shown to you when having completed the assignment is only a white screen and you are not able to follow the explanations the professor is providing. I would really appreciate if the invisible slides would be available for download but this is not the case. All in all I am a little bit mixed about the course as for example particle filters are just mentioned in one video but not explained as all the various types of Kalman filters. Still I give this course a 5-star ranking as it provides a good starting point for those trying to dig deeper into SLAM.

By Hemanth K K

May 23, 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.

By Carlos A

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.

By Muhammad H S H J I

Aug 12, 2019

Very interesting course if you want to learn about the different filters used in self driving cars for sensor fusion

By carlos s

Dec 5, 2019

Excellent course! The best course available of this topic

By anis

Dec 6, 2019

a very good course about sensor fusion ans localization

By Georgios T

Jul 30, 2019

Very helpful!

By Yuwei W

Nov 17, 2019


By D.B

Apr 5, 2020

The course content is good, the instructors are good, and the projects are good. But I hate the quizzes and notebooks throughout the course that don't provide better guidance or step-by-step solution checking. It would be much better overall if quizzes and notebooks in the courses either provided step-by-step solution checking or provided the solutions so students could check their work along the way. I’d much prefer the notebooks provide the solutions or most of the solutions and have a difficult final project for each course where there were no solutions given. I’d learn much more through the course and have confidence while completing the final projects, and have a sense of accomplishment that I applied what I learned. I’m so frustrated with this that I’m cancelling my subscription for now.

By Kasra D

Oct 12, 2020

I finished the first course in this specialization and now I'm in the second course. This is mostly a marketed course which doesn't have much academic value. They just have emphasized on beautiful slides and graphics. The content is shallow and doesn't go into details. If you are looking for a course that you learn the concepts well, this course is not for you.

By Andrea B

Jun 16, 2020

too much high level, no in depth treatments of the topics. There are free course avaiable online which are more in depth than this (paid) course

By Dane R

Jul 6, 2020

Great coverage of state estimation without getting the student bogged down in gratuitous math and proofs. The videos cover enough for you to implement the algorithms and the referenced additional readings will help guide you to learn more depth on the subject if you want (I think it is extremely important to read some of these materials). Simplifying assumptions are stated in the videos so that the student understands how this material deviates from more realistic or advanced implementations - this is a very important point so I was glad to see it clearly explained.

As a critical comment, I would have liked to a little more detail about quaternions to help improve intuition about them. Quaternions are likely new to a lot of people, and they are certainly not intuitive.

By Mukund C

Jun 8, 2020

Amazing course. Highly recommend for understanding some of the foundational blocks for self-driving cars including some workhorse algorithms/principles that are used. Definitely a "weed"-out course and the student has to persevere and draw upon the resources provided in the readings and other resources outside to be able to completely internalize the concept and the math that explains the concepts. However, for students that are willing to put in the effort, there is a lot of learning to be gained on the other side. As a suggestion, the course, should be extended to 7 weeks with some more in-depth discussion into the derivations - specifically on the Unscented Kalman Filter. A guided discussion on one or two fundamental papers will also be fantastic.

By Qi W

Jan 11, 2021

This is not an easy course. If you expect to understand just by watching videos, you are horribly wrong. Definitely read the suggested reading materials and derive things on paper as you go, at least that's how I learned. What I like about this course is that it provides me a relatively comprehensive introduction to sensor fusion (except for camera data, which will be covered in another course in the series). I wish there are more practices with radar data. In fact I had to decide to take Udacity Sensor Fusion courses mainly for that reason because radar will just become more and more important for true L4 and L5 in my humble opinion.

By Parikshit M

Mar 31, 2020

A very thoughtful introduction to the subject of state estimation and localization. The material introduces sufficient basic material and in adequate depth to equip you to learn more. Don't expect to be writing production level code after finishing this course. The expectation should be to learn enough to venture in the field of state estimation on your own and to be able to understand the material in books, research papers and other resources. The supplementary resources are extremely well selected and provide very good pointers to deepen your knowledge. The exercises are definitely very helpful.

By Yashasvi S

Jun 29, 2020

This course was really amazing! The supplementary material provided really helped me to strengthen my concepts. The lectures were very clear and to the point. This course demands hard work from the student's side too, unlike many other courses on Coursera which literally spoon-feed everything. The discussion forums are really helpful if you use them properly. The programming assignments are challenging and might even leave you frustrated at times. But overall, it's worth the time. I highly recommend this course to people who are interested in self-driving cars

By Ananth R

Jul 30, 2019

An excellent course on state estimation and localization. This course is a hands-on approach to the development and implementation of the Kalman Filter for localization. Parts of the assignments and the final project were challenging and the course needs a lot of self-study. The resources provided on the course proved to be extremely useful throughout, and almost self-sufficient. I highly encourage anybody who's willing to take up a practical challenge in state-estimation to take this course.