Lockheed Martin Robotics Seminar: Robust Perception for Robots

Friday, November 13, 2020
2:00 p.m.
Online seminar. Registration is required.

Registration link


Robust Perception for Robots: Sensor Fusion from Algorithm to Device Design

Dezhen Song
Department of Computer Science and Engineering
Texas A&M University  


Combining multiple sensor modalities to achieve more robust understanding of environment and robot status is an emerging research area in robot navigation and autonomous driving. To fuse sensors such as camera, lidar, inertial measurement unit (IMU), wheel encoder, etc., one must solve problems in synchronization, calibration, signal correspondence, and data fusion. In this talk, I will discuss the recent progress that we have made in sensor fusion to address many problems in autonomous driving and robot navigation using autonomous motorcycle and NASA Robonaut as examples. We will also discuss how augmented reality development on mobile devices benefit from the sensor fusion approach in robotics. Moreover, addressing perception challenges after sensory data are collected from individual modalities may limit perception potential; I will talk about sensor fusion at device level where we combine different sensory modalities into a single device to achieve new promising capabilities in applications such as underwater communication & ranging, and robotic grasping. 


Dezhen Song is a Professor and Associate Department Head for Academics with Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA. Song received his Ph.D. in 2004 from University of California, Berkeley; MS and BS from Zhejiang University in 199 and 1995, respectively. Song's primary research area is robot perception, networked robots, visual navigation, automation, and stochastic modeling. He received NSF Faculty Early Career Development (CAREER) Award in 2007.  From 2008 to 2012, Song was an associate editor of IEEE Transactions on Robotics (T-RO). From 2010 to 2014, Song was an Associate Editor of IEEE Transactions on Automation Science and Engineering (T-ASE). From 2017 to 2020, Song was a Senior Editor for IEEE Robotics and Automation Letters (RA-L). He is also a multimedia Editor and chapter author for Springer Handbook of Robotics. Dezhen Song has been PI or Co-PI on more than $14.0 million in grants including more than $4.0 million from NSF. His research has resulted in one monograph and more than 110 refereed conference and journal publications. 


Mumu Xu

Contact: Ania Picard, appicard@umd.edu

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