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Отзывы учащихся о курсе Robotics: Perception от партнера Пенсильванский университет

4.4
звезд
Оценки: 508
Рецензии: 130

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

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.

LR

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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51–75 из 126 отзывов о курсе Robotics: Perception

автор: qiang l

Dec 30, 2016

Very useful course, interesting topics !

автор: Troy W

May 11, 2016

Very solid material, a must-have course.

автор: Chuck

May 16, 2017

pretty good for vision based perception

автор: Lihao W

Jun 30, 2018

Good course but a little hard for me.

автор: Shawfy

Sep 18, 2018

Extremely wonderful the coursera is.

автор: Walter J

Apr 25, 2016

Apasionante tema. Muy bien expuesto

автор: Yuan J

Aug 15, 2018

excellent course for slam learning

автор: Akhilesh K

Sep 01, 2017

Challenging and enjoyable course!

автор: Guillermo C

Aug 21, 2017

Challenging, very well delivered.

автор: K0r01

Dec 29, 2017

good materials for visual SLAM

автор: jiqirenzhifu

Aug 28, 2017

I have gained a lot 。thank you

автор: Qiu Q

Jun 20, 2016

very good assignment!

автор: Terry Z

Jan 12, 2018

Very useful course!

автор: 徐恩科

Sep 05, 2018

very good course.

автор: Ng B K

Aug 06, 2016

Love the course.

автор: Daniel W

Jan 02, 2017

Great course!

автор: Aryan A

Aug 20, 2018

Great course

автор: 张浩悦

Aug 31, 2017

very

good!!!!

автор: Xu D

May 27, 2019

Love this!

автор: Meshal A

May 30, 2017

Fantastic!

автор: Bálint - H F

Mar 20, 2019

Great !

автор: 杨镑镑

Aug 19, 2016

Great!

автор: Eduardo K d S

Sep 16, 2016

This course is great! There is a lot of information available, a wide range of topics are covered, some complex subjects are explained quite well and especially the Chinese professor makes it easy to understand them.

Still, there is room for improvement. Considering this is a 4-week course and the coverage of the material, sometimes it feels too squeezed and cramped together. This could be improved by providing access to more references to other materials to complement the studies. A bibliography for instance would be much welcome.

Also there are some annoying typos in the slides in the formulas and its derivations that can cost you some precious time to figure out, especially during the Matlab assignments.

These are the only reasons I don't give this course 5 stars, but it's definitely worthwhile. You will not regret it!

автор: Sourav G

Feb 20, 2018

It was really an interesting course and is recommended for those interested in Vision-based applications for their robots, especially dealing with motion estimation, visual odometry, visual SLAM, image matching using local point features (SIFT) etc. The course did help a lot in brushing up some concepts from undergrad and using them to create some amazing codes through assignments.

There are few things that can be improved, for example, some of the videos in the course lack proper explanation and it took a while to understand. Some of the quizzes comprise questions to which answers cannot be derived using the course content (AFAIU). The inverse depth parameterization-based direct pose estimation is not covered (e.g. as in LSD-SLAM).

автор: Liang M

Apr 17, 2017

A very good course in general. The materials and assignments are practical and the explanation of the instructors are clear. You are expect to gain a general knowledge about computer vision, camera calibration, and the usage of linear algebra in computer vision.

One thing that could be improved is that there is a big jump from week 2 to week 3 and also from week 3 to week 4. It's like a sophomore course at week 1 and week 2 and suddenly it jumps to a senior course in week 3 and a graduate course in week 4. It might be better to provide some supplementary materials in between.