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Отзывы учащихся о курсе Robotics: Perception от партнера Пенсильванский университет

4.4
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Оценки: 582
Рецензии: 155

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

SK
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.

LR
31 дек. 2018 г.

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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26–50 из 151 отзывов о курсе Robotics: Perception

автор: Reynaldo M G

13 февр. 2018 г.

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).

автор: Cristian D

17 июня 2017 г.

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).

автор: Nico W

5 февр. 2017 г.

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.

автор: Amit K

31 окт. 2020 г.

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

автор: Anh T

4 нояб. 2018 г.

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.

автор: Charlie ( Y

9 мар. 2020 г.

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

автор: An N

3 нояб. 2016 г.

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.

автор: Salahuddin K

1 апр. 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.

автор: Rajeev K

24 февр. 2018 г.

Course is nicely organized and helps even a novice without much in depth knowledge of image processing to understand the concepts

автор: Joe D

30 нояб. 2016 г.

Awesome material! I think this is the one course of the specialization that had the appropriate amount of work for the timeline.

автор: Srikanth V

17 окт. 2019 г.

It is hard course, thoroughly enjoyed it. Lessons on how to effectively use vanishing points was very useful.

автор: 李晨曦

29 июля 2017 г.

Great lectures! I felt a little confused at the beginning , but everything makes sense by the end of class.

автор: Hamid M

2 мар. 2019 г.

One of the most usefful courses I have taken by the coursera. Thank you for useful materail covered here.

автор: Abdelrhman H N

13 мая 2016 г.

Solid Material as an introductory course and gives glimpse on the new horizons on computer vision.

автор: Liang L

26 дек. 2018 г.

The professors have very detailed description, and the programming assignments are valuable.

автор: Samuel D

12 мая 2016 г.

Very good. Teachers worked hard. Practical and quite comprehensive for such short term.

автор: Lokesh B

27 мая 2018 г.

This was by far the best course. Very difficult and complex. But it is worth studying

автор: Chinthaka A

24 мая 2019 г.

Great course, I been able to develop new skills and knowledge. Highly Recommended.

автор: Abhishek G

20 мар. 2017 г.

Really good course for getting a sound foundation on geometry of computer vision.

автор: Bernardo M R J

13 авг. 2016 г.

Excellent course, very nice field of research with a lot of space for innovation.

автор: Shakti D S

27 апр. 2016 г.

So many interesting concepts and theories, packed in a 4 week course.

Excellent.

автор: Abhiram S

11 янв. 2019 г.

Great course!. There is a lot of information. It should be a 6 week course!

автор: Sunaada H M N

3 дек. 2019 г.

The assignments were challenging and the course videos were really good

автор: Dilshan M

1 февр. 2020 г.

Great in-depth course into the fundamentals of perception in robotics.

автор: Wong H

20 окт. 2016 г.

Good course, lecturers do their best to give u an informative course