Вернуться к State Estimation and Localization for Self-Driving Cars

4.7

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Оценки: 259

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Рецензии: 43

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)....

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.

Feb 09, 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.

Фильтр по:

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: Remon G

•Aug 12, 2019

Very useful!

Great experience!

Congratulation all the people involved in this course!

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: 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: 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: 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.

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: 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: Carlos E S V

•Dec 05, 2019

Excellent course! The best course available of this topic

автор: Anis M

•Dec 06, 2019

a very good course about sensor fusion ans localization

автор: Georgios T

•Jul 30, 2019

Very helpful!

автор: Yuwei W

•Nov 17, 2019

great

автор: 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.

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: 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: Abdullah B A

•Sep 25, 2019

excellent course with a lot of valuable and up to date information that is used in real modern self driving cars, it was challenging and very hard for me to go through but i assure you that it's worthy of the hard work required to pass it

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: Gasser N

•Oct 30, 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.

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: Asad Q

•Feb 09, 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.

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: YanDing

•Feb 01, 2020

Very good course! I learned how to implement multiple sensor fusion into practice. Thank you!

автор: Zaihao W

•Jan 17, 2020

This is the best course that can give me a in-depth understanding on Kalman Filter.

автор: Karthik B K

•Jun 29, 2019

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

автор: Levente K

•Mar 01, 2019

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

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