16-833: Robot Localization and Mapping (Spring 2021)

Day: Monday and Wednesday
Time: 2:20pm-4:40pm
Room: TBD
Lecturer: Michael Kaess
TA office hour: TBD

Robot localization and mapping are fundamental capabilities for mobile robots operating in the real world. Even more challenging than these individual problems is their combination: simultaneous localization and mapping (SLAM). Robust solutions are needed that can handle the uncertainty inherent in sensor measurements, while providing localization and map estimates in real-time. We will explore suitable efficient probabilistic inference algorithms at the intersection of linear algebra and probabilistic graphical models. We will also explore state-of-the-art systems.