Retour à Robotics: Perception

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624 évaluations

How can robots perceive the world and their own movements so that they accomplish navigation and manipulation tasks? In this module, we will study how images and videos acquired by cameras mounted on robots are transformed into representations like features and optical flow. Such 2D representations allow us then to extract 3D information about where the camera is and in which direction the robot moves. You will come to understand how grasping objects is facilitated by the computation of 3D posing of objects and navigation can be accomplished by visual odometry and landmark-based localization....

DA

31 janv. 2021

This course was truly amazing. It was challenging and I learned a lot of cool stuff. It would have been better if more animations were included in explaining complex concepts and equations.

SK

31 mars 2018

Outstanding Course! I could always count on Prof.Jianbo to crunch some of the most complex and confusing parts of the course into a much easier understandable language.

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par Philippe W

•11 janv. 2017

This course is excellent: lots of things covered in depth, learning curve is high, detailed explanations with lots of examples; If you want to ramp up quickly on Structure for Motion or Visual Odometry, Visual SLAM, this is highly recommended. But be prepared to put some real effort in this demanding course. Overall one of the best MOOC I took. Programming assignments and especially the last one are *very* interesting. It's great to have such courses that are available for everybody. Pre-requisites are linear algebra (eigenvalues, eigenvectors, Jacobian, Hessian ...) and familiarity with matlab (but people familiar with numpy should easily ramp up). For people not familiar with matlab there are also some very nice matlab tutorials in the resources. Highly recommended.

par 刘宇轩

•19 déc. 2017

Great Deal of Math.

Prof. Shi's lectures on math guides me through this course. Whenever he shows up in the video, I know he will give me almost everything I need to solve the problems.

Really Intensive and rewarding.

The programming assignment is not that difficult if we have understood the meaning of the equations on the slide.

But the math is not easy. Though Prof. Shi has been giving the lectures in a rather reasonable pace, I still have to pause the videos for quite a long time to follow him on math.

I WILL NEVER FORGET SVD AFTER THIS COURSE. AMAZING!

Hope Coursera can offer more intensive courses like this. Really like courses going in the order of advanced math - algorithm - practice.

par TKor78

•19 nov. 2016

This is the -hands down- best course within the Robotics Specialization. It is educating as well as entertaining (well, as far as a mooc about robotic perception can be, but I loved it!) and you will learn A LOT, if you don't give up and try hard. Because this course is not easy at all. Its not for beginner and sometimes I had the impression that its neither for people with a somewhat intermediate level of engineering and/or mathematical understanding. I struggled about 40 hours with the final project, but in the end I managed to finish successfully! Thanks to the staff for this very cool learning experience!

par Julius S

•17 juil. 2016

As a standalone course - incredible. Lots of content, in detailed guides through maths. You seem to have been structural, but I still go lost every time. Overall impression is very chaotic. Maybe more summaries could help. Also separating videos into 'guide through maths' and ' guide through reasoning/theory/motivation' could help

As a part of specialization: This course has more content than the previous 3 courses combined. This took me by surprise and it actually took me 3 months to finish this course. Maybe you could saparate some stuff to optional and required?

par Awais A

•3 févr. 2021

1-Fundamental Course in Computer Vision. 2-Very helpful and comprehensive course material. 3-Some repetition by instructors. 4-Very interactive slides and Good use of colors for expaling linear algebraic derivations. 5-Comprehensive programming assignments.

Things to improve upon: 1-Sometimes its difficult to comprehend verbal delivery because instructors are not native English speakers.

Remarks: I loved this course and will recommend for beginners in Computer Vision. But keep in mind that it is very demanding course.

par Akshit J

•18 déc. 2019

This course has everything it takes to understand SLAM. The instructors have worked very hard that they start with the basic concepts of perception and work all the way up to components of current SLAM. You will be very well able to appreciate the current code base on ORB-SLAM as code does not involves derivation and this course covers all the linear algebra behind it. Hats off to professors for not skimming through concepts and making sure they convince you without needing to refer to external resources.

par Gui B

•3 mai 2020

Amazing course on computer vision geometry. I did not expect much from this course to begin with, I was expecting a walk along the park instead of deep knowledge I wanted. But! it exceeded my expectations from week 1 and forced me to go look for other materials and refresh my rusty linear algebra to pass the challenging quizzes and assignments. When Professor Shi talks conceptually, it is very easy to picture. The maths just flow naturally once the concepts are understood.

I Highly recommend!

par Tong L

•8 juin 2017

This course is definitely worth learning if you are interested in computer vision or robotics perceptions! There are some minor flaws in the lectures slides, but it doesn't seriously effect the learning experience. I would recommend this course to people who have some basic knowledge about computer vision (e.g. camera calibration, coordinate transformation, affine/rigid transforms, linear solution of structure from motion). Otherwise, the latter part of this course could be a bit difficult.

par Xin T

•28 avr. 2021

I cannot believe I finished it! This course involves a lot of mathematics and requires a lot of brain works. Things taught in this course are what I need to know in my work as a self-driving vehicle software engineer. Both instructors are amazing and know what they are teaching. Especially, Professor Jianbo Shi explain the math equations clearly in great details. I would recommend this course to whoever wants to work on computer vision/ visual perception module of self-driving cars/robots.

par Enrico A

•24 juil. 2017

This course is interesting and very thorough. Some concepts of robot perception are explained in detail, with a focus on perception based on 2D vision. The videos are clear and there is a great number of quizzes and Matlab programming to improve your practical understanding of the topic. Be warned, though, that this course takes longer than 4 weeks in fact due to the numerous and long lectures.

par Jianxin L

•16 nov. 2017

This is a Coursera course with the richest contents I ever had. Very glad to have learned so much in robotic perceptions. Thanks so much to Prof Daniilidis and Prof Shi. it is challenging but also very useful and helpful for further study or research. TAs are also very good helping lots of students. Love this class. Thank you all!

par Islam A A

•22 juil. 2017

The course is very important for any student / engineer working in the field of robotics. It gives a lot of detailed information about the background needed as well as some hands-on experience with the basic tools in computer vision. A very good point is connecting what we study in the course with some real applications.

par Edgar M G

•1 avr. 2018

Excellent Course, at the beginning I was a beginner in this topic and now I learned a lot about computer vision and visual perception. That is a fundamental part of mobil robot localization and planning. As a feedback, I only recomend more numerical examples, this would help to understand more quickly the topics.

par Y S

•24 juin 2020

This is an excellent courses for beginners and for experienced engineers interested in learning the basics of Bundle Adjustment and 3D geometry. The ideas are very well explained and the exercises in Matlab contribute to understanding the concepts taught. I could not recommend this course more highly.

par Reynaldo M G

•13 févr. 2018

This course is a tough one, the assignments are challenging. One problem with teh course is the use of english subtitles, there some errors on mathematical terms that makes more difficult to understand what is being explained (and sometimes the teachers' english is not very clear).

par Cristian D

•17 juin 2017

Course is unusually difficult compared to the others in the series. You'll learn plenty of stuff, though, which is useful not just in robotics itself but many other applications with a mobile camera (such as stitching panoramas taken with your phone, or producing CGI).

par Nico W

•5 févr. 2017

Interesting material, presented well, very on-top and supportive TAs. I wish the second assignment had been the first assignment (the current first assignment is very basic and can be scrapped), so that the 4th assignment could be about implementing bundle adjustment.

par Nukul S

•17 févr. 2021

It is a great course and the material is really good. I understand creating an automatic grader is never easy, but in estimation problems if we do things certain way it will lead to differences, but not necessarily bad results. Wish this could have an easier way :).

par Amit K

•31 oct. 2020

I was looking for a good course on Computer Vision which tells about its basics, Epipolar Geometry, SFM, etc. and found this module under the Robotics course. The course content was really good and explanatory. Thank You,

Amit Kumar

par Anh T

•4 nov. 2018

Extremely challenging... took me 3 months to pass the course. It required me to go to Khan Academy and revise all about Linear Algebra + Derivatives... Especially Null Space and Jacobian ... It's challenging but it's really good.

par Erman N

•25 mai 2022

Edit My review (from 4 to 5):

This course is like an old wine. It gets better with time. I followed the course and got my certificate in 2019, and 3 years later (2022), it remains relevant today as it was the first day.

par Charlie ( Y

•9 mars 2020

use the forums, and re-watch videos with the quiz pulled up

good derivations / walkthrough of spatial concepts behind the math used in various processing done in perception like SFM, working with monocular RGB data

par An N

•3 nov. 2016

Good intro course for someone has no prior knowledge in Computer Vision. The entire course is about linear algebra practices. Professors provide lots of information, assignment projects are interesting.

par David A

•1 févr. 2021

This course was truly amazing. It was challenging and I learned a lot of cool stuff. It would have been better if more animations were included in explaining complex concepts and equations.

par Salahuddin K

•1 avr. 2018

Outstanding Course! I could always count on Prof.Jianbo to crunch some of the most complex and confusing parts of the course into a much easier understandable language.

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