Abstract: The ability of autonomous robot systems to perform reliably and effectively in real-world settings depends on precise understanding of the geometry and semantics of their environment based on streaming sensor observations. This talk will present estimation techniques for sparse object-level mapping, dense surface-level mapping, and distributed multi-robot mapping. The talk will highlight object shape models, octree and Gaussian process surface models, and distributed inference in time-varying graphs.
Bio: Nikolay Atanasov is an Assistant Professor of Electrical and Computer Engineering at the University of California San Diego, La Jolla, CA, USA. He obtained a B.S. degree in Electrical Engineering from Trinity College, Hartford, CT, USA in 2008 and M.S. and Ph.D. degrees in Electrical and Systems Engineering from University of Pennsylvania, Philadelphia, PA, USA in 2012 and 2015, respectively. His research focuses on robotics, control theory, and machine learning, applied to active perception problems for mobile robots. He works on probabilistic models that unify geometry and semantics in simultaneous localization and mapping (SLAM) and on optimal control and reinforcement learning of robot motion that minimizes uncertainty in these models. Dr. Atanasov's work has been recognized by the Joseph and Rosaline Wolf award for the best Ph.D. dissertation in Electrical and Systems Engineering at the University of Pennsylvania in 2015, the best conference paper award at the IEEE International Conference on Robotics and Automation (ICRA) in 2017, and an NSF CAREER award in 2021.
Location: We will meet only virtually. Please use the following zoom meeting information to join us:
Meeting ID: 846 7722 4909