University of Pennsylvania

Robotics: Perception

This course is part of Robotics Specialization

Taught in English

Some content may not be translated

Kostas Daniilidis
Jianbo Shi

Instructors: Kostas Daniilidis

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Course

Gain insight into a topic and learn the fundamentals

4.3

(642 reviews)

|

81%

Intermediate level
Some related experience required
32 hours (approximately)
Flexible schedule
Learn at your own pace

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Assessments

21 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.3

(642 reviews)

|

81%

Intermediate level
Some related experience required
32 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Robotics Specialization
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There are 4 modules in this course

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.

What's included

15 videos1 reading8 quizzes1 programming assignment

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.

What's included

5 videos4 quizzes1 programming assignment

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.

What's included

8 videos5 quizzes1 programming assignment

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.

What's included

14 videos4 quizzes1 programming assignment

Instructors

Instructor ratings
4.0 (46 ratings)
Kostas Daniilidis
University of Pennsylvania
1 Course39,412 learners

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