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Отзывы учащихся о курсе Robotics: Perception от партнера Пенсильванский университет

4.4
звезд
Оценки: 600
Рецензии: 166

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

DA
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.

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.

Фильтр по:

76–100 из 162 отзывов о курсе Robotics: Perception

автор: Qiu Q

19 июня 2016 г.

very good assignment!

автор: Terry Z

12 янв. 2018 г.

Very useful course!

автор: 徐恩科

5 сент. 2018 г.

very good course.

автор: RAMAN S

12 сент. 2020 г.

excellent course

автор: Ng B K

6 авг. 2016 г.

Love the course.

автор: Daniel W

2 янв. 2017 г.

Great course!

автор: Aryan A

20 авг. 2018 г.

Great course

автор: 张浩悦

31 авг. 2017 г.

very

good!!!!

автор: Xu D

27 мая 2019 г.

Love this!

автор: Meshal A

30 мая 2017 г.

Fantastic!

автор: Bálint - H F

20 мар. 2019 г.

Great !

автор: 杨镑镑

18 авг. 2016 г.

Great!

автор: Eduardo K d S

16 сент. 2016 г.

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

20 февр. 2018 г.

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

17 апр. 2017 г.

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.

автор: Berke

11 окт. 2020 г.

This course offers great way to begin with vision based applications such as visual odometry - slam i came this course because i wanted to learn math background about multiview geometry and algorithms such visual odometry and i learned a lot during the course (also made me realize that i need to study more math). The one setback about this course is that some part of this(week 2-3 mostly) course can be improved.

автор: Fernando C

17 мая 2016 г.

This course has a lot of interesting material regarding perception using cameras. The lectures focus on a wide range of topics, from the basics until camera pose estimation, epipolar geometry, optical flow and 3D motion. The explanations are very clear, and the Jacobian explanation using colors is excellent.

Negative points: sometimes the lectures are long, and the concepts are a little bit mixed.

автор: Qirui Z

6 июня 2019 г.

Still some errors in the homework PDF (The codes are all working though). And the curriculum seems a little bit redundant. Also hope there will be more emphasis on emerging applications like visual odometry and SLAM, in stead of spending too much time on the ancient geometry (It's not expected to always have a checkerboard in your image? :-) ).

автор: Matthew P

3 мая 2018 г.

The course was very detailed but perhaps a little too densely packed for a 4 week course. The instructors covered a very wide breadth of material in a very short time. I believe the instruction for the final assignment could be improved, but overall a good class introducing many important concepts in robotics and vision systems.

автор: Shaun K

4 апр. 2017 г.

Loved the lecture and materials. However, the course need more ACTIVE teaching staff and mentors. I had several questions regarding the materials but could not get any help from start to the end. It was the only specialization course that I had to move on without complete understanding of the materials.

автор: Lucila P

4 нояб. 2016 г.

The course has a good structure. It covers interesting themas. The assignments are easy to understand. It takes more effort than 3 or 5 hours/week, the nomenclatur could be improved to be consistent. A couple of more examples would improve learning. The four week was hard ;)

автор: LOKESH G

8 мая 2019 г.

The course content is exceptionally well and material is well designed. Since most of the concepts are in-depth more support will be required in discussion forums. I feel the response from mentors is slow and have to wait days for their reply to clarify the doubts.

автор: Max B

9 мая 2021 г.

Wish there were more reading materials even it there weren't enough time to cover them. Too often, professors jumped from one concept to another without bridging.

Also, please fix typos in the videos and lecture notes.

автор: Soon H Y

26 апр. 2020 г.

PROS: Introduces the most practical and essential concepts and algorithms

CONS: The workload is not evenly distributed across all 4 weeks, lecturer's explanation is mediocre and materials can be hard to follow.

автор: hiback

19 дек. 2018 г.

The lecture is pretty good, learned a lot from it.But there are some bugs in assignment's pdf. Fortunately, our community forum pointed out these errors. Hoping they could fix it for better understanding.