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.
ペンシルベニア大学（University of Pennsylvania）
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
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ROBOTICS: PERCEPTION からの人気レビュー
The content is quite useful but the teaching can be improved upon through shorter videos and more animations instead of hand gestures (or static images) to explain mathematical derivations.
The content is not very easy to understand because the lecture speaks very fast and the document is not very sufficient. But in all, the content is good, help me with my research.
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.
The 4th week content is hard to follow than the previous three. It would be better if more detailed math and examples are provided in the 4th week.
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.
Excellent organization and presentation of the course material, and very prompt responses from the teaching staff on the message boards.
Very good course. The only thing that I would suggest is an higher precence of moderators in the forum. It would be very appreciated.
Course is nicely organized and helps even a novice without much in depth knowledge of image processing to understand the concepts
Awesome material! I think this is the one course of the specialization that had the appropriate amount of work for the timeline.
Subtitles are generated by machines I think. Very many subtitles are wrong. It is very unfriendly to non-English speaking users.
Extremely fast-paced course that gives a great overview of Perception but leaves a lot of things unexplained or without proofs.
It was a very good course, the only thing is the time, I think that was to short in order to cover all the topics more deeply.
Very interesting and useful course. Professors give a lot of information. However, some explanations are not very clear.
For Computer Vision enthusiast who wants to learn about Multiple View Geometry, this is the best beginner course
It is hard course, thoroughly enjoyed it. Lessons on how to effectively use vanishing points was very useful.
Lots of good content, good explanations, and good pictures to explain things. I enjoyed the assignments too
One of the most usefful courses I have taken by the coursera. Thank you for useful materail covered here.
very useful course. However it needs some supplementary materials in math. also more solved examples.
Solid Material as an introductory course and gives glimpse on the new horizons on computer vision.
Very good. Teachers worked hard. Practical and quite comprehensive for such short term.