Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
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<strong>Session</strong> WedFT1 <strong>Pegaso</strong> A <strong>Wednesday</strong>, <strong>October</strong> <strong>10</strong>, <strong>2012</strong>, 16:15–17:30<br />
Estimation and Sensor Fusion<br />
Chair Li-Chen Fu, National Taiwan Univ.<br />
Co-Chair<br />
16:15–16:30 WedFT1.1<br />
Contactless deflection sensing of concave and<br />
convex shapes assisted by soft mirrors<br />
Michal Karol Dobrzynski, Ionut Halasz, Ramon Pericet-Camara<br />
and Dario Floreano<br />
Laboratory of Intelligent Systems,<br />
Ecole Polytechnique Federale de Lausanne, Switzerland<br />
• Deflection sensor capable of concave<br />
and convex shape estimation with no<br />
impact on the deflected substrate<br />
softness.<br />
• Dynamic range of 130° with 0.8°<br />
resolution and 400Hz data acquisition.<br />
• Analytical model in good agreement<br />
with measurements (average error of<br />
8%)<br />
• Novel quick manufacturing method for<br />
soft PDMS mirrors based<br />
on surface tension.<br />
Top: Sensor in standard configuration perceives<br />
concave deflections only; Middle and bottom:<br />
By attaching a customized mirror the range can<br />
be extended towards convex deflections.<br />
16:45–17:00 WedFT1.3<br />
Manipulator State Estimation with Low Cost<br />
Accelerometers and Gyroscopes<br />
Philip Roan and Nikhil Deshpande<br />
Robert Bosch LLC, USA and North Carolina State University, USA<br />
Yizhou Wang and Benjamin Pitzer<br />
University of California, Berkeley, USA and Robert Bosch LLC, USA<br />
• Estimate manipulator joint angles using<br />
triaxial accelerometers and uniaxial<br />
gyroscopes<br />
• Comparing three different compensation<br />
strategies:<br />
• Complementary Filter<br />
• Time Varying Complementary Filter<br />
• Extended Kalman Filter<br />
• Mean error of 1.3° over the joints<br />
estimated, resulting in end-effector errors<br />
of 6.1 mm or less<br />
A generic joint between two links<br />
showing how accelerometers and<br />
gyroscopes are mounted.<br />
17:15–17:30 WedFT1.5<br />
Sensor Fusion Based Human Detection and<br />
Tracking System for Human-Robot Interaction<br />
Kai Siang Ong, Yuan Han Hsu, and Li Chen Fu, Fellow, IEEE<br />
Department of Computer Science & Information Engineering,<br />
Department of Electrical Engineering<br />
National Taiwan University, Taiwan, R.O.C.<br />
• Integrating the information of laser<br />
range finder and that from vision<br />
sensor using Covariance<br />
Intersection algorithm<br />
• Propose a behavior-response<br />
system:<br />
(1) human behavior‘s inference<br />
(2) robot reaction<br />
• For Human Behavior Intention<br />
Inference, we take the proxemics<br />
framework into consideration.<br />
Public Space<br />
Interaction Space<br />
vlrf<br />
d<br />
f<br />
x f , y f<br />
θia , θ fd<br />
16:30–16:45 WedFT1.2<br />
Deformable Structure From Motion by Fusing<br />
Visual and Inertial Measurement Data<br />
Stamatia Giannarou, Zhiqiang Zhang, Guang-Zhong Yang<br />
Hamlyn Centre for Robotic Surgery, Imperial College London, UK<br />
• 3D reconstruction of a deforming surgical<br />
environment in MIS is important for intraoperative<br />
guidance.<br />
• A novel adaptive UKF parameterization<br />
scheme is proposed to fuse vision<br />
information with data from an Inertial<br />
Measurement Unit for accurate 3D<br />
reconstruction.<br />
• A direct application of the proposed<br />
framework is free-form deformation<br />
recovery to enable adaptive motion<br />
stabilization and visual servoing in<br />
robotically assisted laparoscopic surgery.<br />
17:00–17:15 WedFT1.4<br />
Vision-Aided Inertial Navigation<br />
Using Virtual Features<br />
Chiara Troiani and Agostino Martinelli<br />
INRIA Rhone Alpes, France<br />
• MAV equipped with a monocular camera<br />
and a laser pointer mounted on a fixed<br />
baseline and an IMU<br />
• Unique point feature used: laser spot<br />
projected on a planar surface and<br />
observed by the monocular camera<br />
• Analytical derivation of all the observable<br />
modes<br />
• Local decomposition and recursive<br />
estimation of the observable modes<br />
performed using an Extended Kalman<br />
Filter<br />
<strong>2012</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–171–