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GRAND seminar Multi-Robot Simultaneous Localization and Mapping Abstract Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics, and is a major stepping stone towards fully autonomous robots. The SLAM problem arises when a robot does not have a map of its environment and does not know where it is. Using only on-board sensors, the robot builds a map of the environment and simultaneously locates itself in the map. For single robots, there are several robust techniques that have been used in many domains including the DARPA Grand Challenge and RoboCup. The next challenge is to extend SLAM to multiple robots working together. Multi-robot SLAM has three primary challenges: coordination of the group, merging multiple maps together, and limited communication. This talk will review the SLAM problem, and introduce current approaches to the multi-robot SLAM problem. I will also present my ideas for this problem. Biography Keith Sullivan received a BS in Mathematics and BA in Journalism from Indiana University in 1998, a MS in Computer Science from George Mason University in 2005, and is now pursuing a PhD in Computer Science from George Mason University. His research interests include robotics, artificial intelligence and evolutionary computation.
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