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 76~100

교육 기관: Luis E T R

2020년 9월 13일

An outstanding course

교육 기관: Sujeet B

2020년 12월 7일

Lot's of learning...

교육 기관: NAREN B

2020년 5월 28일

Many many thanks!

교육 기관: MIHIR R J

2020년 5월 30일

Very Infomative!

교육 기관: mert s

2019년 10월 31일

excellent course

2021년 3월 2일

just pefect

교육 기관: Arturo A E O

2020년 9월 2일

excelente

교육 기관: Matías F

2021년 1월 18일

amazing!

교육 기관: Jeff L J D

2020년 11월 28일

Thanks

교육 기관: Md. R Q S

2020년 8월 19일

great

교육 기관: Soumyajit M

2020년 10월 5일

Good

교육 기관: Nejc D

2020년 5월 6일

The course covers some interesting and highly important concepts regarding state estimation. I guess the videos are not intended to be a "follow-along" lectures but more of a "these are the topics you should study by yourself" videos. In other words, the videos tend not to go deep, instead only the important results are quickly presented. On the other hand the programming assigments are quite fun.

Reflecting on how much knowledge and understanding somebody needs to show to pass this course I wouldn't rate it as advanced, I would rather say intermediate.

To sum up, this is either a course for somebody who wants to get some basic ideas about state estimation applied to self driving cars or for somebody who wants to dive deep into this topic and wants to use this course as a guidance on his/her self-study journey

교육 기관: Maksym B

2019년 4월 3일

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: Baixiao

2019년 7월 29일

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: Lealem S

2021년 4월 12일

The course is a good fit for someone with some background in navigation. It would have been best if unnecessary content such as history of GNSS/GPS, history of Kalman filter and the "autobiographies'' in the beginning are truncated to make more time for the actual content.

For example one can easily google and learn about the history of GPS, Kalman, etc. This is the kind of material best left as a link to additional resources instead of the links to additional materials for Jacobian derivations and quaternion algebra.

교육 기관: Nicolas Y

2019년 12월 4일

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: mike w c

2019년 6월 18일

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: Shubham R P

2019년 9월 20일

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: Atharva K

2020년 5월 30일

A pretty involving course.

Good points - EKF, UKF explained properly.

Bad points - The weeks 3 onwards course is not sufficiently explained, less mathematics and more intuitive understanding, tough time if you do not have experience with python programming.

교육 기관: Yulia M

2019년 3월 11일

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: Shrutheesh R I

2020년 7월 17일

Thank you for this absolutely fantastic course. Kalman filters and state estimation in general is a concept that I've tried to understand for a long time, and I'm glad to have finally understood it!

교육 기관: Harshal B

2020년 5월 22일

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.

교육 기관: Farid I

2019년 9월 24일

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

2019년 8월 13일

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

교육 기관: Aref A

2019년 6월 26일

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