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.
This course is part of the Robotics Specialization
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Robotics SpecializationUniversity of PennsylvaniaAbout this Course
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Learner Career Outcomes
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started a new career after completing these courses
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Course 4 of 6 in the
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Intermediate Level
Approx. 33 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Skills you will gain
Computer VisionEstimationRandom Sample Consensus (Ransac)Geometry
Learner Career Outcomes
40%
started a new career after completing these courses
29%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 33 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Offered by
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.
Syllabus - What you will learn from this course
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
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)
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)
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)
Reviews
TOP REVIEWS FROM ROBOTICS: PERCEPTION
by ASNov 8, 2019
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.
by DAJan 31, 2021
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.
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.
by SKMar 31, 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.
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.
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