About this course: 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.

Created by: University of Pennsylvania

Taught by: Kostas Daniilidis, Professor of Computer and Information Science
School of Engineering and Applied Science
Taught by: Jianbo Shi, Professor of Computer and Information Science
School of Engineering and Applied Science

Basic Info	Course 4 of 6 in the Robotics Specialization.
Level	Intermediate
Commitment	4 weeks of study, 3-5 hours/week
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.3 stars Average User Rating 4.3See what learners said

Syllabus

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. ...

15 videos, 1 reading

Graded: Introduction

Graded: Vanishing Points

Graded: Perspective Projection

Graded: Rotations and Translations

Graded: Dolly Zoom

Graded: Feeling of Camera Motion

Graded: How to Compute Intrinsics from Vanishing Points

Graded: Camera Calibration

Graded: Dolly Zoom

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 ...

5 videos

Graded: Homogeneous Coordinates

Graded: Projective Transformations

Graded: Vanishing Points

Graded: Cross Ratios and Single View Metrology

Graded: Image Projection using Homographies

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...

8 videos

Graded: Visual Features

Graded: Singular Value Decomposition

Graded: RANSAC

Graded: 3D-3D Pose

Graded: Pose Estimation

Graded: Image Projection

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...

14 videos

Graded: Epipolar Geometry

Graded: Nonlinear Least Squares

Graded: 3D Velocities from Optical Flow

Graded: Bundle Adjustment

Graded: Structure from Motion

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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.

Pricing

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Ratings and Reviews

Rated 4.3 out of 5 of 220 ratings

I give it 4 stars because the material is very interesting and the Home works are pretty cool. Some of the lectures and slides aren't so clear. They cover a huge amount of material which, while a bargain, is also more work than expected. It's tough material! I'm happy I took the class and finished.

Course is unusually difficult compared to the others in the series. You'll learn plenty of stuff, though, which is useful not just in robotics itself but many other applications with a mobile camera (such as stitching panoramas taken with your phone, or producing CGI).

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.

This course is definitely worth learning if you are interested in computer vision or robotics perceptions! There are some minor flaws in the lectures slides, but it doesn't seriously effect the learning experience. I would recommend this course to people who have some basic knowledge about computer vision (e.g. camera calibration, coordinate transformation, affine/rigid transforms, linear solution of structure from motion). Otherwise, the latter part of this course could be a bit difficult.

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