16-833: Robot Localization and Mapping (Spring 2017)

Days: TR
Time: 3pm-4:20pm
Room: NSH 3002
Lecturer: Michael Kaess
TA: Ming Hsiao
TA office hour: Wednesday 3-4pm, NSH 4201

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.