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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

4.7
별점
676개의 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 101~111

교육 기관: 蒋阅

2020년 6월 28일

Need more code example or supplementary reading about python and numpy

교육 기관: Jorge B S

2020년 6월 30일

Some information was really difficult to understand.

교육 기관: Ahmad I B

2020년 7월 31일

Loved Every bit of it. Looking forward to get more

교육 기관: 胡江龙

2019년 5월 6일

good!

교육 기관: flyhigher Y

2020년 7월 5일

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: Metehan S

2021년 5월 10일

This course is good for those who are interested in learning about general concept (not in depth) of State Estimation. It would have been better if ICP topic had been distributed through a whole seperate week and had an coding assignment. One need to learn about Particle Filter too. Other than that I am satisfied .

교육 기관: Karim I

2021년 1월 24일

The content and the projects are good, but a lot of details as derivations, mathematical concepts (like quaternions) and documentation of the project codes are not well covered neither in the course videos nor in the reading materials.

The forums were not very helpful to explain these details.

교육 기관: Salma S L

2020년 3월 26일

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: Mustafa P

2021년 1월 25일

More help should be provided by better lectures and more explanation on the projects.

교육 기관: Wentao T

2020년 5월 17일

too hard, and the data is not good

교육 기관: PRATIK W

2020년 8월 24일

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