Robotics: Perception
date_range Débute le 24 décembre 2018
event_note Se termine le 28 janvier 2019
list 4 séquences
assignment Niveau : Intermédiaire
chat_bubble_outline Langue : Anglais
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Les infos clés

credit_card Formation gratuite
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timer 12 heures de cours

En résumé

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.

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Le programme

  • Week 1 - Geometry of Image Formation
    Welcome to Robotics: Perception! We will begin this course with a tutorial on the standard camera models used in computer vision. These models allow us to understand, in a geometric fashion, how light from a scene enters a camera and projects onto a 2D image. ...
  • Week 2 - Projective Transformations
    Now that we have a good camera model, we will explore the geometry of perspective projections in depth. We will find that this projection is the cause of the main challenge in perception, as we lose a dimension that we can no longer directly observe. In this ...
  • Week 3 - Pose Estimation
    In this module we will be learning about feature extraction and pose estimation from two images. We will learn how to find the most salient parts of an image and track them across multiple frames (i.e. in a video sequence). We will then learn how to use featur...
  • Week 4 - Multi-View Geometry
    Now we will use what we learned from two view geometry and extend it to sequences of images, such as a video. We will explain the fundamental geometric constraints between point features in images, the Epipolar constraint, and learn how to use it to extract th...

Les intervenants

Kostas Daniilidis
Professor of Computer and Information Science
School of Engineering and Applied Science

Jianbo Shi
Professor of Computer and Information Science
School of Engineering and Applied Science


Le concepteur

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.

La plateforme

Coursera est une entreprise numérique proposant des formations en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.

Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.

Avis de la communauté
3.8 /5 Moyenne
Le meilleur avis

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

le 24 février 2018
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Quelle note donnez-vous à cette ressource ?
le 24 février 2018

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

le 20 février 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).

le 13 février 2018

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).

le 6 janvier 2018

Extremely fast-paced course that gives a great overview of Perception but leaves a lot of things unexplained or without proofs.

le 19 décembre 2017

Great Deal of Math.Prof. Shi's lectures on math guides me through this course. Whenever he shows up in the video, I know he will give me almost everything I need to solve the problems.Really Intensive and rewarding.The programming assignment is not that difficult if we have understood the meaning of the equations on the slide.But the math is not easy. Though Prof. Shi has been giving the lectures in a rather reasonable pace, I still have to pause the videos for quite a long time to follow him on math. I WILL NEVER FORGET SVD AFTER THIS COURSE. AMAZING!Hope Coursera can offer more intensive courses like this. Really like courses going in the order of advanced math - algorithm - practice.