Handouts and lecture notes
Week 1
Lecture Notes:
- Introduction: ps
- Hardware and collision avoidance: ps
Readings:
Week 2
Lecture Notes:
- Behavior-based paradigm: ps
- Intro to probabilities: ps
Readings:
- Konolig's Gradient method to
collision avoidance (IROS 2000).
- Further readings on the projects page.
Week 3
Lecture Notes:
- Motion models: ps
- Sensor models: ps, html
Readings:
Week 4
Lecture Notes:
- Robot localization and Kalman filters: ps
Readings:
- Chapter 1,2 of Probabilistic Robotics book.
- The ultimate Kalman
filter web site.
- Very nice tutorial on Kalman
filters by Welch and Bishop.
- Nice 1D example of Kalman filter by
Maybeck.
Week 5
Lecture Notes:
- Discrete representations: ps
- Particle filters: ps
Readings:
Week 6
Lecture Notes:
- Applications of particle filters: ps
Readings:
Week 7
Week 8
Lecture Notes:
- Path planning: ps
- Decision making: ps
Readings: