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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

4.6
192개의 평가
34개의 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

WS

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.

RL

Apr 27, 2019

It provides a hand-on experience in implementing part of the localization process...interesting stuff!! Kind of time-consuming so be prepared.

필터링 기준:

State Estimation and Localization for Self-Driving Cars의 36개 리뷰 중 26~36

교육 기관: Yulia M

Mar 11, 2019

The content of the course is great, very useful and applicable ! The lectures are well told, animations are brilliant. I rate this course as 4 stars due to a low feedback activity from the teaching staff.

교육 기관: Maksym B

Apr 04, 2019

The course has very advanced material and I value this course a lot. However I am very confused at some key concepts and didn't understand many details conceptually. For example it is not clear what is the difference between EKF and ES-EKF.

Also, for the final project the formulas have been given. I implemented the project using the formulas, but I didn't understand deeply enough the meaning of those formulas. For example what does Kalman Gain represent.

Maybe the topic is just so advanced, or maybe I should be reading more resources outside the lectures. But I finished the course with the feeling that I have a lot to learn in the space of localization and state estimation.

교육 기관: 胡江龙

May 07, 2019

good!

교육 기관: mike w c

Jun 18, 2019

There are several errors in the presentations and in the videos, the tutors did not correct them and thus the assignments were very confusing due to stupid math mistakes made by the organizers, it is clear that they are not taking it 100% serious, nonetheless I have seen few courses were they explain State estimation for SDV so good as this one.

교육 기관: Aref A

Jun 26, 2019

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

교육 기관: Huang, B

Jul 29, 2019

Great course that teaches you most of what you need to know about state estimation. What is missing is the state estimation using particle filter, it would be great if there is a module dedicated for that. Some video lectures are little bit confusing, specifically at the error state estimation part, but if you read the provided reading materials, you should be able to understand it more thoroughly. The final project is difficult, you are expected to read some advanced papers on state estimation, but it is very rewarding once you figure out on your own.

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: Shubham R P

Sep 20, 2019

Great course! Very in depth understanding of Kalman Filters and Sensor Fusion. You need to look more literature to understand the concept. Final project is very nice. May be more insight could have been provided about orientation,quaternions and euler angles conversions.

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: Nicolas Y

Dec 04, 2019

This course is wonderful, however, is it quite tough, not only for the technical content but also because I believe it could use some more clarification for the quizzes and other.

All in all, I thought it was a very satisfying way to review old skills and learn new state-of-the-art techniques!

Recommending it heavily, but be ready for frustrations.

교육 기관: Rade

Jun 07, 2019

Very dry lectures!

Quiz automated grader buggy and not working at times. Example: not well defined python environment for the quiz in module 4. A grader expects a certain format that you have to guess. But to guess you need to submit the quiz in order to see if you satisfied the grader. So you can do that 5 times every our. A lot of time spent on satisfying the grader format that learning material.

The reason I am realty trying to stay in the class is because I am very interested in the subject but the execution of this class is a disaster!