This course is part of the Robotics Specialization

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637 ratings

Kostas Daniilidis +1 more instructor

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Robotics SpecializationUniversity of Pennsylvania

About this Course

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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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Course 4 of 6 in the

Robotics Specialization

Intermediate Level

Approx. 32 hours to complete

English

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Skills you will gain

Computer Vision
Estimation
Random Sample Consensus (Ransac)
Geometry

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 6 in the

Robotics Specialization

Intermediate Level

Approx. 32 hours to complete

English

Could your company benefit from training employees on in-demand skills?

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Instructors

Instructor rating

3.86/5 (43 Ratings)

Kostas Daniilidis

Professor of Computer and Information Science

School of Engineering and Applied Science

36,913 Learners

1 Course

Jianbo Shi

Professor of Computer and Information Science

School of Engineering and Applied Science

37,154 Learners

5 Courses

Offered by

University of Pennsylvania

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Syllabus - What you will learn from this course

Content Rating81%(2,009 ratings)

Week1

Week 1

10 hours to complete

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. By defining these models mathematically, we will be able understand exactly how a point in 3D corresponds to a point in the image and how an image will change as we move a camera in a 3D environment. In the later modules, we will be able to use this information to perform complex perception tasks such as reconstructing 3D scenes from video.

10 hours to complete

15 videos (Total 180 min), 1 reading, 9 quizzes

15 videos

Introduction10mCamera Modeling10mSingle View Geometry14mMore on Perspective Projection8mGlimpse on Vanishing Points10mPerspective Projection I14mPerspective Projection II14mPoint-Line Duality8mRotations and Translations18mPinhole Camera Model10mFocal Length and Dolly Zoom Effect8mIntrinsic Camera Parameter13m3D World to First Person Transformation13mHow to Compute Intrinsics from Vanishing Points12mCamera Calibration11m

1 reading

Setting up MATLAB10m

8 practice exercises

Introduction30mVanishing Points30mPerspective Projection30mRotations and Translations30mDolly Zoom30mFeeling of Camera Motion30mHow to Compute Intrinsics from Vanishing Points30mCamera Calibration30m

Week2

Week 2

6 hours to complete

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 module, we will learn about several properties of projective transformations in depth, such as vanishing points, which allow us to infer complex information beyond our basic camera model.

6 hours to complete

5 videos (Total 69 min)

Week3

Week 3

8 hours to complete

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 features to find the position of the camera with respect to another reference frame on a plane using Homographies. We will also learn about how to make these techniques more robust, using least squares to hand noisy feature points or RANSAC to remove completely erroneous feature points.

8 hours to complete

8 videos (Total 126 min)

Week4

Week 4

9 hours to complete

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 the relative poses between multiple frames. We will finish by combining all this information together for the application of Structure from Motion, where we will compute the trajectory of a camera and a map throughout many frames and refine our estimates using Bundle adjustment.

9 hours to complete

14 videos (Total 221 min)

14 videos

Epipolar Geometry I23mEpipolar Geometry II14mEpipolar Geometry III24mRANSAC: Random Sample Consensus II6mNonlinear Least Squares I3mNonlinear Least Squares II6mNonlinear Least Squares III13mOptical Flow: 2D Point Correspondences19m3D Velocities from Optical Flow16m3D Motion and Structure from Multiple Views18mVisual Odometry19mBundle Adjustment I17mBundle Adjustment II18mBundle Adjustment III17m

4 practice exercises

Epipolar Geometry30mNonlinear Least Squares30m3D Velocities from Optical Flow30mBundle Adjustment30m

Reviews

4.3

177 reviews

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TOP REVIEWS FROM ROBOTICS: PERCEPTION

by SMJan 4, 2021

Great course for those who wants to understand how classical SLAM systems work. I think it would be a bit more practical if the assignments were made in python.

by LRDec 31, 2018

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.

by DYMar 27, 2020

The concepts were explained very well and clearly. The last week content seemed a bit complicated to follow, but it was not unsolvable. I enjoyed the course. Thank you!

by SGJun 16, 2017

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.

View all reviews

About the Robotics Specialization

The Introduction to Robotics Specialization introduces you to the concepts of robot flight and movement, how robots perceive their environment, and how they adjust their movements to avoid obstacles, navigate difficult terrains and accomplish complex tasks such as construction and disaster recovery. You will be exposed to real world examples of how robots have been applied in disaster situations, how they have made advances in human health care and what their future capabilities will be. The courses build towards a capstone in which you will learn how to program a robot to perform a variety of movements such as flying and grasping objects.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Robotics: Perception

About this Course

Skills you will gain