Robot Perception |
TAKAGI Motoki |
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【 Informatics Science〈Correspondence Course〉(Master's Degree Program)･full year】
19-3-1728-4737 |
| １． |
Outline |
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We learn how to control Robot system using information acquired from video camera.
This course is related to DP3.
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| ２． |
Objectives |
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(1) We will learn image processing method with python
(2) We will learn pin hole camera model.
(3) We will learn how to control robot with camera
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| ３． |
Grading Policy |
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Evaluation rate are Report 20 %, midterm exam 40%, final exam(40%).
All the reports should be submitted.
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| ４． |
Textbook and Reference |
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No textbook in this course, but we use LMS and handouts.
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| ５． |
Requirements (Assignments) |
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Preparation for the class: 1.5 hours
Review of the class ： 1.5 hours
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| ６． |
Note |
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Course contents might be modified.
|
| ７． |
Schedule |
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１. Introduction to Robot Perception
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| ２. Introduction to vector and matrix for computer vision with python |
３. Computer Vision(1) Introduction to Image processing with OpenCV
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| ４. Computer Vision(2) Introduction to Image processing using filters |
５. Computer Vision(3) Introduction to camera model and camera parameters
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６. Computer Vision(4) Introduction to camera calibration
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７. Computer Vision(5) Introduction to stereo image, epipolar geometry
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８. Computer Vision(6) Introduction to stereo camera calibration
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９. Computer Vision(7) Introduction to 3D reconstruction from stereo cameras
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10. Computer Vision(8) Processing image sequence from camera
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11. Computer Vision(9) Camshift tracker，Kalman filter
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12. Robot Control(1) Introduction to ROS
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| 13. Robot Control(2) Practicing to use ROS with GAZEBO |
14. Robot Control(3) Practising to use ROS+OpenCV
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15. Robot Control(4) Practising to use ROS+OpenCV+Gazebo
|
|
| １． |
Outline |
|
We learn how to control Robot system using information acquired from video camera.
This course is related to DP3.
|
| ２． |
Objectives |
|
(1) We will learn image processing method with python
(2) We will learn pin hole camera model.
(3) We will learn how to control robot with camera
|
| ３． |
Grading Policy |
|
Evaluation rate are Report 20 %, midterm exam 40%, final exam(40%).
All the reports should be submitted.
|
| ４． |
Textbook and Reference |
|
No textbook in this course, but we use LMS and handouts.
|
| ５． |
Requirements (Assignments) |
|
Preparation for the class: 1.5 hours
Review of the class ： 1.5 hours
|
| ６． |
Note |
|
Course contents might be modified.
|
| ７． |
Schedule |
|
１. Introduction to Robot Perception
|
| ２. Introduction to vector and matrix for computer vision with python |
３. Computer Vision(1) Introduction to Image processing with OpenCV
|
| ４. Computer Vision(2) Introduction to Image processing using filters |
５. Computer Vision(3) Introduction to camera model and camera parameters
|
６. Computer Vision(4) Introduction to camera calibration
|
７. Computer Vision(5) Introduction to stereo image, epipolar geometry
|
８. Computer Vision(6) Introduction to stereo camera calibration
|
９. Computer Vision(7) Introduction to 3D reconstruction from stereo cameras
|
10. Computer Vision(8) Processing image sequence from camera
|
11. Computer Vision(9) Camshift tracker，Kalman filter
|
12. Robot Control(1) Introduction to ROS
|
| 13. Robot Control(2) Practicing to use ROS with GAZEBO |
14. Robot Control(3) Practising to use ROS+OpenCV
|
15. Robot Control(4) Practising to use ROS+OpenCV+Gazebo
|
|
|