![]() ![]() Week 36: Chapter 2 State Estimation & Chapter 3.2 - Gaussian Filters - Kalman Filter Week 36: Chapter 1 - Introduction & Robot Paradigms The assignments were based on the Octave or Matlab environment, ported to Jypiter notebooks. Students, who were not able to attend a lecture, can catch up by listing to the recordings of Prof. The course will take place in a studio classroom setting. The assignments of this course are designed to understand the basic problems concerning mobile robotics. ![]() Their suggestion is to accompany the book with a number of practical, hands-on assignments for each chapter. The book concentrates on the algorithms, and only offers a limited number of exercises. This course is based on the book 'Probabilistic Robotics', from Sebastian Thrun, Wolfram Burgard and Dieter Fox. By doing so, it accommodates the uncertainty that arises in most contemporary robotics applications. It relies on statistical techniques for representing information and making decisions. Probabilistic robotics is a subfield of robotics concerned with the perception and control part. More details about the organization of the course can be found in the Course Manual. The course is a constrained choice in the Master Artificial Intelligence Curriculum. ![]() The description is available in the course catalogue of the UvA This course was given for the first time on Master level in ( Fall 2017). Note that the course will no longer be given in 2019. Course Probabilistic Robotics Master Artificial Intelligence This is the information of Fall 2018 ![]()
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