学院机器人与信息自动化研究所 天津市智能机器人技术重点实验室

Institute of Robotics and Automatic Information System

Tianjin Key Laboratory of Intelligent Robotics

2021年秋季先进机器人与人工智能系列学术讲座（第192期）

Seminar Series：Advanced Robotics & Artificial Intelligence

报告时间：2021年12月3日（周五）10:00a.m.~11:30a.m.（Beijing Time）

腾讯会议：245 597 940（会议密码：3311；会议直播：https://meeting.tencent.com/l/IbI4Uc0XnGBf）

报告题目：Visual-Inertial Systems: Estimation, Perception and Navigation

报告人：黄国权 教授

专家单位：University of Delaware（美国特拉华大学）

报告摘要：

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 remain. In particular, many state estimation algorithms of perception and navigation 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. It becomes even more challenging to design efficient consistent estimation and perception algorithms when sensing, processing and memory resources available to vehicles are limited. In this talk, I will present our recent research efforts on taking up these challenges. I will first discuss the observability-based methodology for consistent state estimation in the context of simultaneous localization and mapping (SLAM) and visual-inertial navigation system (VINS), and then will highlight some of our recent results on visual-inertial estimation and perception, including OpenVINS, inertial preintegration for graph-based VINS, robocentric visual-inertial odometry, Schmidt-EKF for visual-inertial SLAM with deep loop closures, visual-inertial moving object tracking and many others. Note that many of these codebases have been open sourced.

报告人简介：

Guoquan (Paul) Huang is an Associate Professor of Mechanical Engineering (ME), Computer and Information Sciences (CIS), and Electrical and Computer Engineering (ECE), at the University of Delaware (UD), where he is leading the Robot Perception and Navigation Group (RPNG). He is also an Adjunct Professor (Control Science and Engineering) at the Zhejiang University and Principal Research Scientist at Meituan. 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. (2012) in Computer Science from the University of Minnesota. From 2003 to 2005, he was a Research Assistant (Electrical Engineering) at the Hong Kong Polytechnic University. His research interests focus on state estimation and spatial AI for robotics, including probabilistic sensing, localization, mapping, perception and navigation of autonomous ground, aerial and underwater vehicles. He is an Associate Editors for IEEE Robotics and Automation Letters (RA-L) and IET Cyber-Systems and Robotics (CSR), as well as the two robotics flagship conferences (ICRA and IROS). He was the Program Committee for top robotics/AI conferences such as RSS, AAAI, and IJCAI. He has published about 100 papers in top robotics conferences and journals. Over the years, Dr. Huang has received various honors and awards including the 2015 UD Research Award (UDRF), 2015 NASA DE Space Research Seed Award, 2016 NSF CRII Award, 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, 2019 Winner for the IROS FPV Drone Racing VIO Competition, 2020 Sigma Xi member, IEEE Senior member, and was the Finalists for the RSS 2009 Best Paper Award and the ICRA 2021 Best Paper Award.