Chevron Left
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

4.7
별점
677개의 평가
112개의 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

JC
2021년 2월 9일

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.

WS
2019년 10월 13일

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.

필터링 기준:

State Estimation and Localization for Self-Driving Cars의 111개 리뷰 중 26~50

교육 기관: James L

2019년 4월 12일

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.

교육 기관: RAVI A

2020년 5월 1일

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.

교육 기관: Rama C R V

2020년 4월 19일

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.

교육 기관: Abdullah B A

2019년 9월 25일

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

교육 기관: Mario d R C

2020년 11월 14일

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.

교육 기관: Himanshu B

2019년 7월 12일

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.

교육 기관: Shashank K S

2020년 9월 22일

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.

교육 기관: Kushagra S

2020년 6월 19일

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

교육 기관: Daniele C

2020년 7월 30일

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

2019년 10월 30일

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

2019년 3월 10일

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: Ju-Hsuan C

2021년 2월 10일

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.

교육 기관: Molin D

2020년 11월 10일

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).

교육 기관: Mohammad N M

2020년 5월 22일

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: Jithesh

2020년 11월 22일

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

교육 기관: Jairo G

2020년 11월 26일

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.

교육 기관: Davide C

2019년 5월 18일

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

교육 기관: Matthias P

2020년 6월 13일

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

2020년 9월 6일

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

교육 기관: Eshan M H

2020년 5월 25일

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

교육 기관: YanDing

2020년 2월 1일

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

교육 기관: Teja k

2020년 10월 22일

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

교육 기관: Swapnil N

2020년 9월 2일

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

교육 기관: Tahir I

2020년 6월 4일

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

교육 기관: Paulo E R J

2020년 9월 7일

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