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Отзывы учащихся о курсе State Estimation and Localization for Self-Driving Cars от партнера Торонтский университет

4.6
Оценки: 148
Рецензии: 25

О курсе

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)....

Лучшие рецензии

RL

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.

MI

Aug 12, 2019

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

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1–25 из 25 отзывов о курсе State Estimation and Localization for Self-Driving Cars

автор: Jon H

Jun 05, 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%.

автор: Guruprasad M H

Apr 29, 2019

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

автор: Himanshu B

Jul 12, 2019

Got to learn about many concepts like least squares, Kalman filter, GNSS/INS sensing, LIDAR Sensing. Programming assignments were the most difficult part of this course. And definitely going towards the next course in the specialization.

автор: Remon G

Aug 12, 2019

Very useful!

Great experience!

Congratulation all the people involved in this course!

автор: 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.

автор: Aditya B

Jul 01, 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.

автор: Yusen C

Mar 10, 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!

автор: Levente K

Mar 01, 2019

Sometimes hard, but still pretty much fun to solve all the problems :)

автор: James L

Apr 12, 2019

This is a fast paced course on state estimation. ES Kalman Filter is the focus of the final project. Lectures cover basics of Kalman filter very thoroughly. You need to spend quite some time to sort out complexity to finish the final project, yet the efforts are well spent. You will only graph the fundamentals after hard projects. Overall, a very well organized and executed course. Highly recommended.

автор: 刘宇轩

Apr 25, 2019

The projects are useful enough

автор: 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.

автор: Karthik B K

Jun 29, 2019

Really Advanced and Challenging Course with great scope of gaining knowledge.

автор: Davide C

May 18, 2019

Finishing this course was quite challenging, but I did it. Thanks a lot to the professors for the clear explanations.

автор: Georgios T

Jul 30, 2019

Very helpful!

автор: 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.

автор: 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

автор: Stefan M

Aug 16, 2019

From my point of view a very interesting and well prepared course.

автор: Yulia M

Mar 11, 2019

The content of the course is great, very useful and applicable ! The lectures are well told, animations are brilliant. I rate this course as 4 stars due to a low feedback activity from the teaching staff.

автор: Maksym B

Apr 04, 2019

The course has very advanced material and I value this course a lot. However I am very confused at some key concepts and didn't understand many details conceptually. For example it is not clear what is the difference between EKF and ES-EKF.

Also, for the final project the formulas have been given. I implemented the project using the formulas, but I didn't understand deeply enough the meaning of those formulas. For example what does Kalman Gain represent.

Maybe the topic is just so advanced, or maybe I should be reading more resources outside the lectures. But I finished the course with the feeling that I have a lot to learn in the space of localization and state estimation.

автор: mike w c

Jun 18, 2019

There are several errors in the presentations and in the videos, the tutors did not correct them and thus the assignments were very confusing due to stupid math mistakes made by the organizers, it is clear that they are not taking it 100% serious, nonetheless I have seen few courses were they explain State estimation for SDV so good as this one.

автор: Aref A

Jun 26, 2019

Content is great but lack of instructor support makes the course hard to understand.

автор: Huang, B

Jul 29, 2019

Great course that teaches you most of what you need to know about state estimation. What is missing is the state estimation using particle filter, it would be great if there is a module dedicated for that. Some video lectures are little bit confusing, specifically at the error state estimation part, but if you read the provided reading materials, you should be able to understand it more thoroughly. The final project is difficult, you are expected to read some advanced papers on state estimation, but it is very rewarding once you figure out on your own.

автор: sheraz s

Aug 13, 2019

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

автор: 胡江龙

May 07, 2019

good!

автор: Rade

Jun 07, 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!