Welcome to ICN9005 Robotic Vision
Recent Projects
News
Introduction
This course covers fundamental and a advanced domains in vision for mobile robots, including topics from early vision to mid- and high-level vision. This course will be
in parallel with the MIT 2.166- Autonomous Vehicle(http://duckietown.mit.edu/), with the focus on perceptions.
Staff
Instructor: Nick Wang, email: hchengwang@csail.mit.edu
Teaching Assistant: Brain Chuang, email: fire594594594@gmail.com
Students: Students in Electrical and Computer Engineering (ECE) and Computer Science (CS) are encouraged to join. It is a plus for students who have experience in image processing and computer vision, but not required.
Information
syllabus
Lectures: 2EFG-EE113
Office time:
Activity
midterm/ final exams
In-class quiz
Problem sets
Project
Project
Students will work in teams, with 2-3 people who have diverse background for collaborative efforts in hardware and software. Each team can choose either 1) a challenge in “Duckietown” or 2) your choice of project to improve methods to an existing problem or consider non-traditional problems. Students will present critique projects as well as write and review research paper at the end of the term. Sample Applications:
- Assistance for the blind and visually impaired
- Collision warning systems for mobile robots
- Systems to improve roadside personnel safety at night
- Perceptions for autonomous vehicle, such as lane, traffic light, and street name detection
Staff
Instructor: Nick Wang, email: hchengwang@csail.mit.edu
Teaching Assistant: Brain Chuang, email: fire594594594@gmail.com
Students: Students in Electrical and Computer Engineering (ECE) and Computer Science (CS) are encouraged to join. It is a plus for students who have experience in image processing and computer vision, but not required.
Information
syllabus
Lectures: 2EFG-EE113
Office time:
Activity
midterm/ final exams
In-class quiz
Problem sets
Project
Project
Students will work in teams, with 2-3 people who have diverse background for collaborative efforts in hardware and software. Each team can choose either 1) a challenge in “Duckietown” or 2) your choice of project to improve methods to an existing problem or consider non-traditional problems. Students will present critique projects as well as write and review research paper at the end of the term. Sample Applications:
- Assistance for the blind and visually impaired
- Collision warning systems for mobile robots
- Systems to improve roadside personnel safety at night
- Perceptions for autonomous vehicle, such as lane, traffic light, and street name detection
