Autonomous Systems-Localization, Navigation and Mapping Systems

Undergraduate

This course offers a comprehensive exploration of the fundamental concepts, technologies, algorithms, and methodologies essential for enabling autonomous systems to autonomously navigate, localize themselves, and create accurate maps of their environment. The course will show the theoretical foundations and will also have a considerable experimental component based on Matlab/ROS. The basic concepts in probability followed by probabilistic approaches for data fusion such as Bayes Filters, Kalman Filter, Extended Kalman Filter, Unscented Kalman Filters, and Particle Filters will be provided. The course will also introduce the SLAM problem, showing how this has recently been solved using batch optimization and graph methods. Finally, mapping algorithms will be briefly discussed. This course is one of the four specialization courses introduced in the fourth year and is specific to the area of specialization of Autonomous Vehicles. Prerequisites: GENG-2220, ELEC-3240, MECH-4212. (This is an experiential learning course.)