Overview - Sensing and Perception
Intended learning outcomes
After successfully completing this module the participant will be able to
- Outline the basic operation principle behind some commonly used sensors
- Describe strengths and weaknesses of these sensors
- Discuss problem types addressed in computer vision
- Outline basic principles for and challenges in positioning and mapping
- Apply PyTorch or other framework for deep learning based perception
- Describe some common algorithms used in the field
- Justify decisions in the design of a solution to a problem related to sensing and perception
- Present own work to others
A further goal is that the participant strengthens the network both at the host university and between universities. To achieve this goal, we require that you attend the sessions that are organized and participate in the group projects.
Module overview
The start point is the self-study material which we expect you to have looked at before the 2-day session in LiU - Sensing and Perception so that you are prepared for the group work that will follow.
Examination
To pass the assignment for the part "Sensing and Perception" you need to complete the following assignments (also listed on the Modules page).
- Assignment Positioning sales pitch
- Assignment Object detection
- Assignment Participate M1 Lund
- Assignment Activity Recognition
- Assignment Sensor fusion with GPS and IMU
The deadline defined in the respective assignment is what counts if there are conflicts between different dates in different places.
Time budget
A rough guesstimate for the time you will spend on this module and where is given below.
- Self-study 8h
- Task: Sales Pitch 4h
- 2 day session 16h
- Task: CV-object recognition 4h+16h
- Task: IMU/GPS 16h
- Task: Activity Recognition 16h
Sums to 80h