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Voltar para State Estimation and Localization for Self-Driving Cars

Comentários e feedback de alunos de State Estimation and Localization for Self-Driving Cars da instituição Universidade de Toronto

736 classificações
119 avaliações

Sobre o curso

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)....

Melhores avaliações


29 de out de 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.


9 de fev de 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.

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101 — 119 de 119 Avaliações para o State Estimation and Localization for Self-Driving Cars

por Harshal B

22 de mai de 2020

A well-taught course by Prof. Jonathan Kelly.I accumulated huge amount of knowledge after undergoing his teachings.The supplementary readings proved to be of great help to ace the final project.

por Farid I

24 de set de 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

por Sheraz S

13 de ago de 2019

For new learners, this course provides the beginner to intermediate knowledge. The explanation with examples are quite interesting and easy.

por Aref A

26 de jun de 2019

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

por chaitanya j

28 de jan de 2022

the course really amazing the study material in this course was extremely in-depth.

por 蒋阅

28 de jun de 2020

Need more code example or supplementary reading about python and numpy

por Jorge B S

30 de jun de 2020

Some information was really difficult to understand.

por Ahmad I B

31 de jul de 2020

Loved Every bit of it. Looking forward to get more

por David E L

12 de out de 2021

Excelente curso!

por 胡江龙

6 de mai de 2019


por flyhigher Y

5 de jul de 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.

por Metehan S

10 de mai de 2021

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 .

por Karim I

24 de jan de 2021

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.


21 de set de 2021

IMHO, assignments throughout all courses of the specialization are overcomplicated. I would appreciate more insights and intuitions about the subject rather than pain from calculation Jacobians and translating raw math formulas to Python code.

por Salma S L

26 de mar de 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

por Mustafa P

25 de jan de 2021

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

por Wentao T

17 de mai de 2020

too hard, and the data is not good

por Bourama T

22 de jun de 2022

good course


24 de ago de 2020

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