Abstract:

As autonomous vehicles are emerging in many different application domains from self-driving cars and drone delivery to underwater survey, state estimation, as one of the most important enabling technologies for autonomous systems, becomes more important than ever before. While tremendous progress in autonomous navigation has been made in the past decades, many challenges still remain. For example, many current sate estimation algorithms of robot localization tend to become inconsistent (i.e., the state estimates are biased and the error covariance estimates are different from the true ones), causing mission failure in a short period of time. If resources available to vehicles are limited, designing consistent efficient estimators becomes even more challenging. In this talk, I will present some of our recent work on taking up these challenges. I will discuss our observability-based methodology for improving estimation consistency, and deep learning for loop closure, in the context of simultaneous localization and mapping (SLAM) and visual-inertial navigation system (VINS). In particular, I will highlight our recent results on visual-inertial state estimation and its extensions.

Bio:

Guoquan (Paul) Huang is currently an Assistant Professor of Mechanical Engineering (ME), Electrical and Computer Engineering (ECE), and Computer and Information Sciences (CIS), at the University of Delaware (UD), where he is leading the Robot Perception and Navigation Group (RPNG). He also holds an Adjunct Professor position at the Zhejiang University, China. He was a Senior Consultant (2016-2018) at the Huawei 2012 Laboratories and a Postdoctoral Associate (2012-2014) at MIT CSAIL (Marine Robotics). He received the B.Eng. (2002) in Automation (Electrical Engineering) from the University of Science and Technology Beijing, China, and the M.Sc. (2009) and Ph.D. (2013) in Computer Science from the University of Minnesota. From 2003 to 2005, he was a Research Assistant with the Department of Electrical Engineering, Hong Kong Polytechnic University. His research interests include sensing, localization, mapping, perception and navigation of autonomous ground, aerial, and underwater vehicles. Dr. Huang received the 2006 Academic Excellence Fellowship from the University of Minnesota, 2011 Chinese Government Award for Outstanding Self-Financed Students Abroad, 2015 UD Research Award (UDRF), 2016 NSF CRII Award, 2017 UD Makerspace Faculty Fellow, 2018 SATEC Robotics Delegation (one of ten US experts invited by ASME),  2018 Google Daydream Faculty Research Award, 2019 Google AR/VR Faculty Research Award, and was the Finalist for the 2009 Best Paper Award from the Robotics: Science and Systems Conference (RSS).

 LCSR Seminar Video Link