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

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