MS-CS-L Archives

September 2012


Options: Use Monospaced Font
Show Text Part by Default
Condense Mail Headers

Message: [<< First] [< Prev] [Next >] [Last >>]
Topic: [<< First] [< Prev] [Next >] [Last >>]
Author: [<< First] [< Prev] [Next >] [Last >>]

Print Reply
Fri, 28 Sep 2012 12:23:31 -0400
text/plain; charset=ISO-8859-1; format=flowed
George Mason University
Jyh-Ming Lien <[log in to unmask]>
text/plain (97 lines)
[Apologies for multiple postings]

Please notice the unusual time and venue.
October 01, 11 AM, Monday, ENGR 4801

*    GRAND Seminar


Embedded Visual Perception for Navigation and Manipulation


October 01, 11 AM, Monday
ENGR 4801


Darius Burschka
Univ.-Prof. Dr.-Ing.
Telerobotics and Sensor Data Fusion
Technical University Munich, Germany


Jana Kosecka


I will present the work of the Machine Vision and Perception Group at
TUM in the field of navigation and object registration on a variety of
systems including our flying and manipulation platforms.

Sensing is essential for autonomy in robotic applications. Our focus
is on how to provide sensing to low power systems that enables them to
cope with the high dynamics of the underlying hardware. A disadvantage
of using compact, low-power sensors is often their slower speed and
lower accuracy making them unsuitable for direct capture and control
of high dynamic motion. On the other hand, the inherent instability of
some systems (e.g. helicopters or quadrotors), their limited on-board
resources and payload, their multi-DoF design and the uncertain and
dynamic environment they operate in, present unique challenges both in
achieving robust low level control and in implementing higher level
functions. We developed tracking algorithms (AGAST) and localization
(Z_inf) techniques that can be used for navigation on embedded
systems. I will show their application on OMAP3 processors
( system).

Perception of the sensors can be boosted by adding external data in
form of sensor data fusion or indexing to external databases. I will
present an efficient 3D object recognition and pose estimation
approach for grasping procedures in cluttered and occluded
environments. In contrast to common appearance-based approaches, we
rely solely on 3D geometry information. Our method is based on a
robust geometric descriptor, a hashing technique and an efficient,
localized RANSAC-like sampling strategy.

Short bio:

Darius Burschka received the PhD degree in Electrical and Computer
Engineering in 1998 from the Technische Universität München in the
field of vision-based navigation and map generation with binocular
stereo systems. In 1999, he was a Postdoctoral Associate at the Yale
University, New Haven, Connecticut, where he worked on laser-based map
generation and landmark selection from video images for vision-based
navigation systems. From 1999 to 2003, he has been an Associate
Research Scientist at the Johns Hopkins University, Baltimore,
Maryland. From 2003 to 2005, he was an Assistant Research Professor in
Computer Science at the Johns Hopkins University. Currently, he is an
Associate Professor in Computer Science at the Technische Universität
München, Germany, where he heads the Machine Vision and Perception
group. He was an area coordinator in the DFG Cluster of Excellence
``Cognition in Technical Systems''. He is the head of the virtual
institute TUM-DLR on "Telerobotics and Sensor Data Fusion" since 2005.

His areas of research are sensor systems for mobile robots and human
computer interfaces. The focus of his research is vision-based
navigation and three-dimensional reconstruction from sensor data.

Jyh-Ming Lien
Assistant Professor, George Mason University

MASC Group: