Robotics: Perception

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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 D…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

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 课程作业

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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.

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. 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.


15 videos, 1 reading expand


  1. Video: Introduction
  2. Video: Camera Modeling
  3. Video: Single View Geometry
  4. Video: on Perspective Projection
  5. Video: Glimpse on Vanishing Points
  6. Video: Perspective Projection I
  7. Video: Perspective Projection II
  8. Video: Point-Line Duality
  9. Video: Rotations and Translations
  10. Video: Pinhole Camera Model
  11. Video: Focal Length and Dolly Zoom Effect
  12. Video: Intrinsic Camera Parameter
  13. Video: 3D World to First Person Transformation
  14. Video: How to Compute Intrinsics from Vanishing Points
  15. Video: Camera Calibration
  16. 阅读: Setting up MATLAB

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 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.


5 videos expand


  1. Video: Vanishing Points; How to Compute Camera Orientation
  2. Video: Compute Projective Transformations
  3. Video: Projective Transformations and Vanishing Points
  4. Video: Cross Ratios and Single View Metrology
  5. Video: Two View Soccer Metrology

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 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 videos expand


  1. Video: Visual Features
  2. Video: Singular Value Decomposition
  3. Video: RANSAC: Random Sample Consensus I
  4. Video: Where am I? Part 1
  5. Video: Where am I? Part 2
  6. Video: Pose from 3D Point Correspondences: The Procrustes Problem
  7. Video: Pose from Projective Transformations
  8. Video: Pose from Point Correspondences P3P

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 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.


14 videos expand


  1. Video: Epipolar Geometry I
  2. Video: Epipolar Geometry II
  3. Video: Epipolar Geometry III
  4. Video: RANSAC: Random Sample Consensus II
  5. Video: Nonlinear Least Squares I
  6. Video: Nonlinear Least Squares II
  7. Video: Nonlinear Least Squares III
  8. Video: Optical Flow: 2D Point Correspondences
  9. Video: 3D Velocities from Optical Flow
  10. Video: 3D Motion and Structure from Multiple Views
  11. Video: Visual Odometry
  12. Video: Bundle Adjustment I
  13. Video: Bundle Adjustment II
  14. Video: Bundle Adjustment III

Graded: Epipolar Geometry
Graded: Nonlinear Least Squares
Graded: 3D Velocities from Optical Flow
Graded: Bundle Adjustment
Graded: Structure from Motion
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