probabilistic robotics by sebastian thrun

(A student may be asked to type their assignments if grading is not possible.) This work can be handwritten with the assumption that these assignments are legible. Many of the assignments will be written assignments due on Wednesdays at the beginning of class on the due date specified. While reading assignments will not be directly assigned it is important that students use the textbook to supplement their understanding of the material presented in the lecture. Homework will be assigned regularly in the course. Any announcements regarding the course will be made via the course webpage or in class so be sure to check it frequently and be sure to get material for any class you miss. Students are encouraged to use email and office hours frequently. Students are encouraged to communicate frequently with the instructor regarding any issues with the course. It has a Raspberry Pi processor and a laser rangefinder. The robot, TwoPi, is built on a Create platform from iRobot. Tom is available on an as-needed basis for help with the robot. If requesting an appointment, please send an email suggesting some possible times for appointments. The textbook for the course is Probabilistic Robotics, by Sebastian Thrun, Wolfram Burgard, and Dieter Fox, MIT Press (2006) ISBN: 978-2-9 Some knowledge of linear algebra will be helpful. Prerequisites: Comp 131 or permission of instructor. These techniques will then be applied to topics such as localization, mapping, planning, and control. The first part of the course will introduce the Kalman filter and related Bayesian filters for state estimation. This course is a continuation of COMP 131 (Artificial Intelligence) with an emphasis on probabilistic methods that can be applied to robotics.

Probabilistic robotics by sebastian thrun