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> WedBT1 <strong>Pegaso</strong> A <strong>Wednesday</strong>, <strong>October</strong> <strong>10</strong>, <strong>2012</strong>, 09:30–<strong>10</strong>:30<br />
Sensor Fusion<br />
Chair Reid Simmons, Carnegie Mellon Univ.<br />
Co-Chair Magnus Jansson, KTH Royal Inst. of Tech.<br />
09:30–09:45 WedBT1.1<br />
Ground Plane Feature Detection in Mobile<br />
Vision-Aided Inertial Navigation<br />
Ghazaleh Panahandeh, Nasser Mohammadiha, Magnus Jansson<br />
KTH Royal Institute of Technology, Sweden<br />
• The hardware of the mobile system consists of a monocular camera<br />
mounted on an inertial measurement unit (IMU).<br />
• Exploiting the complementary nature of the IMU-camera sensor fusion<br />
system for estimating the camera translation and rotation, the developed<br />
algorithm consists of two parts:<br />
I. Homography-based outlier rejection<br />
II. Normal-based outlier rejection<br />
<strong>10</strong>:00–<strong>10</strong>:15 WedBT1.3<br />
IEEE/RSJ IROS <strong>2012</strong> Digest Template<br />
Gaussian Process for lens distortion modeling<br />
Pradeep Ranganathan and Edwin Olson<br />
Computer Science and Engineering, University of Michigan, USA<br />
Contributions:<br />
• Incorporate a GP model into a factor<br />
graph inference framework optimized for<br />
Gaussian factor potentials.<br />
• Model evaluation based on test image set.<br />
• GP distortion models achieve accuracy<br />
comparable to the best parametric model.<br />
• GP models provide an implicit but rigorous<br />
framework for automatically determining<br />
distortion model complexity.<br />
09:45–<strong>10</strong>:00 WedBT1.2<br />
Sensor Fusion for<br />
Human Safety in Industrial Workcells<br />
Paul Rybski, Peter Anderson-Sprecher, Daniel Huber,<br />
Chris Niessl, and Reid Simmons<br />
The Robotics Institute, Carnegie Mellon University, USA<br />
• We present a sensor-based approach<br />
for ensuring safety of people in<br />
proximity to robots.<br />
• Our approach fuses data from multiple<br />
3D sensors into an evidence grid.<br />
• People and robots are surrounded by a<br />
safety and danger zone, respectively.<br />
• Impending intersections between safety<br />
and danger zones are identified and the<br />
robot is stopped.<br />
<strong>2012</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–129–<br />
The person’s position is registered by the<br />
3D sensors (shown in green) and is<br />
compared against the 3D volume filled by<br />
the robot (shown in red).<br />
<strong>10</strong>:15–<strong>10</strong>:30 WedBT1.4<br />
Distributed Altitude and Attitude Estimation<br />
from Multiple Distance Measurements<br />
Maximilian Kriegleder, Raymond Oung and Raffaello D’Andrea<br />
Institute for Dynamic Systems and Control, ETH Zurich, Switzerland<br />
• Distance sensors are attached to<br />
known positions on a rigid body.<br />
• Attitude and altitude may be<br />
estimated directly when sensors are<br />
centrally measurable.<br />
• For distributed sensor networks, this<br />
approach is modified to a scalable,<br />
distributed scheme.<br />
• In the limit of sharing information<br />
between sensor nodes, the<br />
estimates approach those<br />
obtainable in a centralized system.<br />
Each module of the Distributed Flight Array<br />
obtains a distance measurement and<br />
communicates with its neighbours.