Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm

Session WedET5 Gemini 2 Wednesday, October 10, 2012, 15:00–16:00
Personal Robots II
Chair Adriana Tapus, ENSTA-ParisTech
Co-Chair
15:00–15:15 WedET5.1
Semantic Object Maps for Robotic Housework –
Representation, Acquisition and Use
Dejan Pangercic¹, Moritz Tenorth², Benjamin Pitzer¹, Michael Beetz³
Robert Bosch LLC-USA¹, IRC ATR-Japan², University of Bremen-Germany³
� End-to-end system for autonomous
building of Semantic Object Maps (SOMs)
for service robots operating in household
environments
� SOMs store functional properties of
objects, articulation models and textured
3D meshes
� SOMs are represented as symbolic
knowledge bases with facts about the
objects in the environment
� SOMs are expressive and powerful
resource of knowledge for service robots
� Tested on PR2 robot + Kinect
15:30–15:45 WedET5.3
Playmate Robots That Can Act
According to a Child's Mental State
Kasumi Abe, Akiko Iwasaki, Tomoaki Nakamura
and Takayuki Nagai
Department of Mechanical Engineering and Intelligent Systems,
The University of Electro-Communications, Japan
Ayami Yokoyama, Takayuki Shimotomai,
Hiroyuki Okada and Takashi Omori
Department of Electrical Engineering, Tamagawa University, Japan
•We propose a playmate robot that
can play with a child.
•The playmate robot should sustain a
child‘s interest in play and forge a
good relationship between the robot
and the child.
•We observed child’s play with a
kindergarten teacher.
Action selection model
•The robot estimates the child's inner
state and select an appropriate action
according to the model.
model
15:15–15:30 WedET5.2
A framework for the design of person following
behaviors for social mobile robots
Consuelo Granata and Philippe Bidaud
Institut des Systèmes Intelligents et
Robotique , UPMC-CNRS UMR 7222
• Framework architecture combining three
layers: perception, decision and control
layers.
• Laser+camera based detector and
estimation of person’s state by EKF
• Decision-making engine exploiting fuzzy
rules to define robot behavior when
dealing with uncertainties
• System experimented in everyday-life
environments to test performance in
different contexts
15:45–16:00 WedET5.4
Planar Segmentation from Depth Images using
Gradient of Depth Feature
Bashar Enjarini and Axel Gräser
IAT Institute, Bremen University, Germany
• Introducing Gradient of Depth Feature
(GoD) used for segmenting planar
regions.
• The GoD feature is parameter-less
• Implementing the GoD feature in robust
algorithm for segmenting depth images.
• The proposed algorithm is robust to
parameter changes.
• The proposed algorithm is able to
segment planar regions from non-planar
objects as well (bottles, cylinders)
• The proposed algorithm meet the
segmentation quality of other stat-of-art
algorithms
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–165–
Example of segmenting a
clustered scene using the
proposed algorithm