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Course Probabilistic RoboticsBachelor Artificial IntelligenceThis is the information of Fall 2011This is the information of the previous course (Spring 2011).
DescriptionThe description is available in the course catalogue with code BAIPR6. The course is a free choice in the Bachelor Artificial Intelligence Curriculum. ContentsProbabilistic robotics is a subfield of robotics concerned with the perception and control part. It relies on statistical techniques for representing information and making decisions. By doing so, it accommodates the uncertainty that arises in most contemporary robotics applications.This course is based on the book 'Probabilistic Robotics', from Sebastian Thrun, Wolfram Burgard and Dieter Fox. The book concentrates on the algorithms, and only offers a limited number of exercises. Their suggestion is to accompany the book with a number of practical, hands-on assignments for each chapter. The assignments of this course are designed to understand the basic problems concerning mobile robotics, and are based on the assignments that accompany the book 'Robotics Primer' of Maja Matarić. Maja's book and assignments are loosely coupled, so Maja's book is not required (although fun to read). The assignments will take place in a virtual world (don't tell Maja). Julian Kooij will assist with the assignments this year. ScheduleThe official schedule should be found here. The course will take place in A1.30, which we will use as studio class room. A list of Frequent Asked Questions will be maintained. Students, who were not able to attend a lecture, can catch up by listing to the recordings of my (Dutch) or Burghard's lectures (English). Download Lecturnity Player and listen to lecture, synchronized with the sheets.
Week 44: Chapter 1 & 2 - Introduction & Robot Paradigms & State Estimation
Week 44: Chapter 3.2 - Gaussian Filters - Kalman Filter
Week 45: Chapter 3.3 - 3.5 Extended Kalman Filters
& Chapter 4 - Nonparametric Filters
Week 46: Chapter 5 - Wheeled Locomotion & Robot Motion Models
Week 46: Chapter 6 - Sensors & Robot Perception Models
Week 47: 'Partial Exam' of Chapter 1-6 of the book, Wednesday November 23, 13:00-15:00, G4.15.
Week 48: Recap - Kalman Filter Geometric Approach (Slides and Dutch recording)
Week 48: Chapter 7 - Markov and Gaussian Localization
Week 48: Chapter 8 - Particle Filters, Grid
and Monte Carlo localization, including KLD-sampling.
Week 49:
Chapter 9 - Occupancy Grid Mapping
Week 50:
Chapter 10 - SLAM
.
Week 50:
Chapter 17 - Exploration, including (UvA approach Week 50: Final - Sebastian Thrun's TED talk.
Week 51: Partial Exam ('open book') of Chapter 7-11 & 17 of the book, Thursday December 22, 13:00-15:00, G4.15.
Week 13: Reexam ('open book'), February 27, 15:00-17:00
Literature
Sebastian Thrun, Wolfram Burgard and Dieter Fox, Probabilistic Robotics, The MIT Press, 2005. Reading guide
Embedding in AI curriculumThis course is supported by the following chapters of 'Artificial Intelligence - A Modern Approach' 3rd edition, by Stuart Russell and Peter Norvig:
Evaluation
The course was this year evaluated by the participants with a 7.3: Links
Last updated December 3, 2012
This web-page and the list of participants to this course is maintained by
Arnoud Visser
(arnoud@science.uva.nl)
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6 visitors in August 2007 | arnoud@science.uva.nl |