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

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 76~89

교육 기관: Farid I

Sep 25, 2019

Challenging course, specially the assignments. The extra literature resources are great. The explanations and examples on the videos could improve. Step by step Hands On examples would fit great

교육 기관: 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.

교육 기관: Aref A

Jun 26, 2019

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

교육 기관: 蒋阅

Jun 28, 2020

Need more code example or supplementary reading about python and numpy

교육 기관: Jorge B S

Jun 30, 2020

Some information was really difficult to understand.

교육 기관: Ahmad I B

Jul 31, 2020

Loved Every bit of it. Looking forward to get more

교육 기관: 胡江龙

May 07, 2019


교육 기관: Hongfei Y

Jul 06, 2020

Very informative about the definition and application about EKF at self driving car. However, I am a lidar engineer who want to know more mathematical and application details about how the lidar ToF data are translated to help with the localization, step by step...

On the other hand, videos kindly provided some of the derivation results of the ESEKF going to be implemented into final project. But the arithmetic process of the Quaternion calculation is quite confusing for the first-time learner and the professor didn't clearly explain the meaning of the algebras used in the videos, such as Cns, q(), capital omega, etc... which cost much unnecessary search time for me to figure them out.

Overall, this is a good course in Coursera Unlimited.

교육 기관: Salma S L

Mar 26, 2020

some equations weren't explained and remained ambiguous to me, needs more explanation on the mathematical side, other than that a great course and great effort

교육 기관: Wentao T

May 17, 2020

too hard, and the data is not good

교육 기관: PRATIK W

Aug 24, 2020

The coding part for each assignment should be explained in more detail

교육 기관: D.B

Apr 05, 2020

The course content is good, the instructors are good, and the projects are good. But I hate the quizzes and notebooks throughout the course that don't provide better guidance or step-by-step solution checking. It would be much better overall if quizzes and notebooks in the courses either provided step-by-step solution checking or provided the solutions so students could check their work along the way. I’d much prefer the notebooks provide the solutions or most of the solutions and have a difficult final project for each course where there were no solutions given. I’d learn much more through the course and have confidence while completing the final projects, and have a sense of accomplishment that I applied what I learned. I’m so frustrated with this that I’m cancelling my subscription for now.

교육 기관: Kasra D

Oct 12, 2020

I finished the first course in this specialization and now I'm in the second course. This is mostly a marketed course which doesn't have much academic value. They just have emphasized on beautiful slides and graphics. The content is shallow and doesn't go into details. If you are looking for a course that you learn the concepts well, this course is not for you.

교육 기관: Andrea B

Jun 16, 2020

too much high level, no in depth treatments of the topics. There are free course avaiable online which are more in depth than this (paid) course