Lockheed Martin Robotics Seminar: Dr Nikolay A. Atanasov, "Autonomous Exploration and Mapping"
Friday, March 1, 2019
2121 JM Patterson
301 405 4358
Lockheed Martin Robotics Seminar Series
Autonomous Exploration and Mapping using Visual, Inertial and Semantic Information
Nikolay A. Atanasov
Department of Electrical and Computer Engineering
University of California, San Diego
Dr Mumu Xu
Recent years have seen impressive progress in robot perception, including accurate visual-inertial odometry, dense metric reconstruction, and object recognition in real time. Surprisingly, however, most existing approaches to simultaneous localization and mapping (SLAM) rely on low-level geometric features and do not take advantage of object-level information. This talk will focus on a unifying view of geometry, semantics, and data association in SLAM. A major contribution of our approach is the use of structured object-models to build meaningful maps online and probabilistic data association that avoids making hard, potentially wrong associations between semantic features and objects in ambiguous environments. We show that the complexity of incorporating probabilistic data association in SLAM is equivalent to computing matrix permanents. Finally, we will consider the active SLAM problem in which a team of robots aims to build a rich environment model autonomously. This problem offers promising applications in environmental monitoring, search and rescue, security and surveillance, and precision agriculture. We will discuss how to manage the complexity of active SLAM with respect to long planning horizons and large robot teams. These results lead to computationally scalable, non-myopic algorithms with quantified performance for exploration and autonomous mapping.
Nikolay A. Atanasov is an Assistant Professor at the Department of Electrical and Computer Engineering, University of California, San Diego, CA, USA. His research focuses on robotics, control theory, and machine learning and in particular on autonomous information collection using ground wheeled and aerial rotorcraft robots for localization and mapping, environmental monitoring, and security and surveillance. He works on probabilistic environment models that unify geometry and semantics and on optimal control and reinforcement learning approaches for minimizing 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 and the best conference paper award at the International Conference on Robotics and Automation in 2017.