16-833: Robot Localization and Mapping (Spring 2018)

Days: MW
Time: 1:30pm-2:50pm
Room: NSH 1305
Lecturer: Michael Kaess
TAs: Paloma Sodhi and Rick Goldstein
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.