University of Minnesota
Computer Science & Engineering
http://www.cs.umn.edu/

CS&E Profile: Stergios Roumeliotis

Stergios Roumeliotis

Associate Professor
(612) 626-7507
Office: Keller 5-189 & 471 Walter
stergios [at] cs.umn.edu
Personal Home Page

Interests

Distributed Robotics, Autonomous Vehicle Navigation, Sensor Networks, Fault Detection and Identification, and Human- robot Interaction.

Education

Ph.D. 2000, M.S. 1999 Electrical Engineering, University of Southern California.

Diploma of Engineering 1995, Electrical Engineering, National Technical University of Athens.

About

Associate Professor Roumeliotis specializes in inertial navigation of aerial and ground autonomous vehicles, fault detection and identification, and sensor networks. Recently his research has focused on distributed estimation under communication and processing constraints and active sensing for reconfigurable networks of mobile sensors.

Dr. Roumeliotis is the recipient of the McKnight Land-Grant Professorship award and the NASA Tech Briefs award. He is currently serving as Associate Editor for the IEEE Transactions on Robotics.

Research

My research is concentrated on sensing and estimation techniques - analysis, modeling and fusion - for autonomous vehicle navigation. Specific examples of these platforms are wheeled Mars rovers, tracked vehicles, unmanned helicopters and spacecrafts, and their domain of application spans from indoors to outdoors and from autonomous landing to planetary exploration. These estimation algorithms are necessary to support intelligent operation such as perception and representation of the environment, planning under uncertainty, autonomous robot navigation and control.

It is within the focus of my research to develop efficient probabilistic algorithms for real-time state estimation from noisy and uncertain sensor information. Over the past few years I have been working on extensions of the state estimation problem to the case of heterogeneous groups of mobile robots operating under communication and processing limitations. The same theoretical framework is also appropriate for dealing with similar issues pertinent to distributed teams of autonomous vehicles and reconfigurable arrays of networked sensors or intelligent embedded systems.

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