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Отзывы учащихся о курсе Robotics: Perception от партнера Пенсильванский университет

Оценки: 558
Рецензии: 146

О курсе

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....

Лучшие рецензии


Apr 01, 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.


Jan 01, 2019

This is quite challenging course. So far, this is the course with the largest amount of material, I wish the class will be split into two courses.

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101–125 из 142 отзывов о курсе Robotics: Perception

автор: Jesus F

Oct 20, 2016

Good course, but assignmets are too long, difficult and with no much help. Workload is overpassed

автор: Xiaotao G

Dec 16, 2018

It is hard compared to previous courses and need more time on it. But quite helpful!

автор: Rahul D

Mar 30, 2020

I was expecting Some implementation of the SFM pipeline from OpenCV or OpenMVG.

автор: Mike Z

Oct 10, 2018

Really good topic but the material can be improved a lot more.

And it's free !

автор: Shubham W

Aug 13, 2017

Excellent course!! Especially Bundle Adjustment was covered in good details.

автор: Ricardo A R

Feb 14, 2019

Need more videos for final weeks, hard to follow last week of the course

автор: Daniel C

Dec 23, 2018

To put it simply: Shi's content is good and Danniilidis' content is bad.

автор: Aman B

Jan 29, 2019

It was interesting, but damn the lectures are never ending.

автор: yanghui

Oct 27, 2017

a bit difficult to understand, anyway,finally passed!

автор: xiao z

May 03, 2020

need specific feed backs for those quizzes!!!

автор: li q

Aug 10, 2016

The lecture notes should be better organized.

автор: Hussain M A

Oct 01, 2019

Hard course but lots of good insight.

автор: Martin X

Oct 23, 2016

The courses are good and helpful.

автор: Ali M H

Oct 16, 2018

Thank you Professors !

автор: Jeffrey

Feb 18, 2017

Unclear explaination

автор: Fredo P C

Feb 03, 2019

Great Course!

автор: Daniel S

May 20, 2017

This course could use some help. It's a very interesting and important topic and is also difficult, but it could be explained better and the tie in between the lecture videos, quizzes and homework assignments could also be better. Some of the quiz questions are not answerable from reviewing the lecture notes and require outside knowledge of linear algebra and rotation mathematics. The assignments should also be better defined and set up so that there is incremental feedback available for the intermediate steps. For example, the last week's assignment has 5 steps, each of which requires a Matlab function to be written. In many online courses, there are "correct" intermediate results given so that each step can be verified before proceeding to the next step. In this assignment, there is not much feedback until you get to the third or fourth step and even then it's not the best. I had an error in one of the functions, but the problem feedback (photo comparisons) showed it as being OK until I submitted it for grading. It's important, since there's no instructor feedback , to provide some means of checking if you're doing things correctly.Some of the terminology used would be more clear if it was standardized; sometimes coordinates are x and y, sometimes u and v, there's also u1, u2, u3 and things like X = [x,y,z,w] and x = [u,v,w]. Its often quite difficult to know what's being referred to it's called x. I did learn a lot from this course, but it could have been a lot easier.

автор: Rishabh B

Jun 10, 2016

The course is a very good overall description of the Perception field. The part I really liked is that there was no haste or a concept just superficially discussed - lectures are long and detailed. The presentation of lectures especially from Prof. Jianbo Shi are excellent - to represent Matrices in colours and give a intuitive sense of every formula(especially the Jacobians and treating the image blending process as painting) .

The bad part of this course is that pronunciations of faculties could be a little unclear and hence a very good transcript is required - which in this course is not upto the mark. There were few mistakes on the slides and should be rectified atleast in the pdf of the slides. What this means is that we have to go through some frustration while watching the video first time which gradually improves on second or third view. Also, there is absolutely no participation of teaching staff. A good content should be supplemented with assistance to further enhance learning experience. Few doubts because of this remains unclear and I wish I could have got this sorted in this class.

автор: carlos r l

May 14, 2016

I dont like how this course was presented. The professors are good but the way how they present the course is extremely inefficient. I mean, because the instructor only speaks moving hands from one side to other, it was very difficult to visualize what and where the instructor was referencing to. Eg. a figure with 3 formulas and many variables there was no way to know in what alpha variable in formulas the instructor was talking about, once all formulas had the alpha variable. Also, when trying to describe a 3D environment only moving hands, its quite impossible to determine what and where the instructor is. One suggestion to try to minimize this problem would be try to use a lase pointer or a stick or a pen or something similar to help the student to now where the instructor exactly is. One example of good presentation is the course of ML from Andrew Ng where he writes all the things while speaking which facilitates the student to follow the sequence. Hope this can help.

автор: Timothy M

Jul 09, 2017

some interesting material. The Slides for week 2 and 4 are terrible, too condensed with very little explanation on difficult topics. The Homeworks are pretty interesting, the assignments for week 3 and 4 complement eachother very well. the week 2 Kalman filter assignment didn't seem to work. I submitted something in frustration and was very surprised that it was accepted.

автор: Vladimir K

Jun 15, 2016

I really loved the dense collection of relevant information, this course is a great introduction to computer vision-related algorithms.

Unfortunately the lecture videos are poorly edited and subtitles are inaccurate, however the slides are quite good and verbose enough to understand every topic.

Assignments are quite good, however formula derivation explanations could be better.

автор: Tiandong W

Jul 02, 2019

It's a nice introduction, but a lot of details are not well explained. A lot of typos in homework, epecially in Equations. This is very bad for UX. And few of mentors are maintaining this course. If you ask questions in forum, you can not get repsonse quickly. I suggest that mentors should spend some time to correct typos and upload some supplement materials.

автор: Ray.Gong

May 03, 2018

The content is undoubtedly valuable and instructive, but for some topics in week 3 & 4 the content isn't organized in a good order. Most importantly, no one answers my questions in the forum, and I felt helpless when the lecturer and slides fail to clearly explain some complex concepts.

автор: Martin Z

Mar 06, 2019

There were a lot of error in the materials even after all those years. Also the instructor's English is hard to understand sometimes. In addition to that they do a lot of waving around with their hands instead of marking things directly in the pictures.

автор: Fabio B

Aug 04, 2017

The course is excellent is computer vision! The only problem it is not didactic at all, so if you don't are familiar with this content it will be very hard (even impossible) to follow.