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State Estimation and Localization for Self-Driving Cars(으)로 돌아가기

토론토 대학교의 State Estimation and Localization for Self-Driving Cars 학습자 리뷰 및 피드백

556개의 평가
90개의 리뷰

강좌 소개

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)....

최상위 리뷰


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.

필터링 기준:

State Estimation and Localization for Self-Driving Cars의 89개 리뷰 중 26~50

교육 기관: Daniele C

Jul 30, 2020

One of the best courses I had on Coursera. Some modules are apparently easy and fast, but the whole course should be well understood in order to pass the final assignment. I had to go back and forth for th

교육 기관: 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.

교육 기관: 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!

교육 기관: 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.

교육 기관: Mohammad N M

May 22, 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!

교육 기관: 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.

교육 기관: Matthias P

Jun 13, 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...

교육 기관: Aaryaman B

Sep 06, 2020

great course but there's really a big need to provide assistance in assignments like hints, equations etc

교육 기관: Eshan M H

May 25, 2020

Challenging, interesting and intriguing.. In simple, an awesome course for any engineering mind !

교육 기관: YanDing

Feb 01, 2020

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

교육 기관: kolla t

Oct 22, 2020

great experience and learned a lot more for the extension of self driving cars course 1

교육 기관: Swapnil N

Sep 02, 2020

please give some coding notes or some codes that only matter with current assignments

교육 기관: Tahir I

Jun 04, 2020

it is definitely worth your time , if you are interested in self driving cars/robots

교육 기관: Paulo E R J

Sep 07, 2020

Awesome course, i've learnt a lot about sensors, kalman filters and sensor fusion!

교육 기관: Zaihao W

Jan 17, 2020

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


Sep 29, 2020

Very Good Lectures and as well as Presentations Thank you for offering this course

교육 기관: Karthik B K

Jun 29, 2019

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

교육 기관: Mehran R

Sep 15, 2020

It requires a bit of external studying, but in general, it's a great course.

교육 기관: Levente K

Mar 01, 2019

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

교육 기관: 78 F V R S

Oct 15, 2020

I had an amazing experience got to learn new things from this course

교육 기관: Ahmed E

Apr 12, 2020

This course was very useful. It will significantly help in my career

교육 기관: Stefan M

Aug 16, 2019

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

교육 기관: Kosinski K

May 25, 2020

The great course! Very good presentations and nice projects.

교육 기관: UMAR T

Mar 04, 2020

The last assignment for this module is very challenging.

교육 기관: Akash B

Jun 16, 2020

Course was good, need more guidance for calculations.