Chevron Left
Zurück zu State Estimation and Localization for Self-Driving Cars

Bewertung und Feedback des Lernenden für State Estimation and Localization for Self-Driving Cars von University of Toronto

4.7
Sterne
736 Bewertungen
119 Bewertungen

Über den Kurs

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-Bewertungen

GN

29. Okt. 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.

JC

9. Feb. 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.

Filtern nach:

26 - 50 von 119 Bewertungen für State Estimation and Localization for Self-Driving Cars

von zhen l

12. Apr. 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.

von Ravi A

1. Mai 2020

This course provides a lot of insights in various sensors used for pose estimation and also delves into multi sensor fusion which gives the knowledge and importance about the sensor calibration. Overall a very well taught course and the most important one for who want to pursue a career in self driving cars.

von Rama C R V

19. Apr. 2020

Firstly, I would like to start thanking Prof. Jonathan Kelley for making good illustration. I felt it could be better discussing more about sizes of covariance matrices, so that it would help in better understanding of the algebra. Overall a good taught and informative course. Thank you Coursera.

von Abdullah B A

25. Sep. 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

von Mario d R

14. Nov. 2020

Excellent course. You go from learning the basic concept of state estimation and localization all the way to solving a realistic state estimation problem. The course is quite dynamic, mixing theoretical concepts with real implementation.

von Himanshu B

12. Juli 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.

von Shashank K S

22. Sep. 2020

Quite a mathematically extensive course, but how the instructors teach will clear all your doubts! The concepts taught apply not only to Self Driving Cars but for any general system. All in all, an excellent course for State Estimation.

von Kushagra S

19. Juni 2020

The programming assignments given tested us on how well we understood the fundamentals of localization. The solutions were not trivial and one had to think while programming which speaks to how well these assignments were designed

von Daniele C

30. Juli 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

von Gasser N

30. Okt. 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.

von Yusen C

10. März 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!

von Ju-Hsuan C

10. Feb. 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.

von Molin D

10. Nov. 2020

Very good to learn Kalman Filter, and Extened Kalman Filter, espcially the good explanation on why it is effective, and restriction (when it is noise, etc).

von Mohammad N M

22. Mai 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!

von Jithesh

22. Nov. 2020

Well Planned course. Giving introduction level details to domain State estimation and localization. Very great detail of Kalman Filter available.

von Jairo G

26. Nov. 2020

Really interesting content and test. Definitely there are lots of advance concepts, so you will need to dedicate quite a lot of time to success.

von Eric J

14. Dez. 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.

von Davide C

18. Mai 2019

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

von Matthias P

13. Juni 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...

von Aaryaman B

6. Sep. 2020

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

von Eshan M H

25. Mai 2020

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

von Yan D

1. Feb. 2020

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

von Teja k

22. Okt. 2020

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

von Swapnil N

2. Sep. 2020

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

von Tahir I

4. Juni 2020

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