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Отзывы учащихся о курсе Robotics: Perception от партнера Пенсильванский университет

Оценки: 624

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


31 янв. 2021 г.

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.


31 мар. 2018 г.

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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126–150 из 173 отзывов о курсе Robotics: Perception

автор: Jesus F

20 окт. 2016 г.

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

автор: G G

18 февр. 2022 г.

Amazing , will give you deep understanding on workings of the 3D reconstruction pipelines

автор: Xiaotao G

16 дек. 2018 г.

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

автор: Rahul D

29 мар. 2020 г.

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

автор: Mike Z

10 окт. 2018 г.

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

And it's free !

автор: Shubham W

13 авг. 2017 г.

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

автор: Jean K G R

24 янв. 2021 г.

In some activities, the theory wasn't enough to complete the assignments

автор: Ricardo A R

14 февр. 2019 г.

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

автор: Daniel C

23 дек. 2018 г.

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

автор: Bhavya G G

20 апр. 2021 г.

Very detailed course. Need to think on your to clear all the concepts

автор: Aman B

29 янв. 2019 г.

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

автор: yanghui

27 окт. 2017 г.

a bit difficult to understand, anyway,finally passed!

автор: Ákos G

13 сент. 2020 г.

Good course, but the video subtitles are garbage.

автор: xiao z

3 мая 2020 г.

need specific feed backs for those quizzes!!!

автор: li q

10 авг. 2016 г.

The lecture notes should be better organized.

автор: Luming

22 сент. 2020 г.

a little difficult for me,but learn a lot!

автор: Hussain M A

1 окт. 2019 г.

Hard course but lots of good insight.

автор: Martin X

23 окт. 2016 г.

The courses are good and helpful.

автор: Ali M

16 окт. 2018 г.

Thank you Professors !

автор: Yafei H

18 февр. 2017 г.

Unclear explaination

автор: Fredo C

3 февр. 2019 г.

Great Course!


14 авг. 2021 г.

so good

автор: Daniel S

20 мая 2017 г.

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

10 июня 2016 г.

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

14 мая 2016 г.

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.