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> WedET1 <strong>Pegaso</strong> A <strong>Wednesday</strong>, <strong>October</strong> <strong>10</strong>, <strong>2012</strong>, 15:00–16:00<br />
Omnidirectional Vision and Aerial Robotics II<br />
Chair Friedrich Fraundorfer, ETH Zurich<br />
Co-Chair Vincenzo Lippiello, Univ. di Napoli Federico II<br />
15:00–15:15 WedET1.1<br />
Vision-only estimation of wind field strength and<br />
direction from an aerial platform<br />
Richard J. D. Moore, Saul Thurrowgood<br />
and Mandyam V. Srinivasan<br />
Queensland Brain Institute, University of Queensland, Australia<br />
• Novel method for estimating wind<br />
field strength and direction from a<br />
moving airborne platform using only<br />
visual information.<br />
• Iterative optimisation allows wind<br />
field properties to be determined<br />
from successive measurements of<br />
aircraft ground track and heading<br />
direction.<br />
• Results from simulated and realworld<br />
flight tests demonstrate<br />
accuracy and robustness of<br />
proposed approach and practicality<br />
for measuring wind in real-world<br />
environments.<br />
Attitude, heading, and ground track are<br />
estimated from omnidirectional visual<br />
input. Relationship between ground track<br />
and heading direction is used to compute<br />
wind field strength and direction.<br />
15:30–15:45 WedET1.3<br />
Vision-Based Autonomous Mapping and<br />
Exploration Using a Quadrotor MAV<br />
Friedrich Fraundorfer, Lionel Heng, Dominik Honegger,<br />
Gim Hee Lee, Lorenz Meier, Petri Tanskanen, Marc Pollefeys<br />
Computer Vision and Geometry Lab, ETH Zürich, Switzerland<br />
• We show vision-based autonomous<br />
mapping and exploration with our MAV in<br />
unknown environments.<br />
• A downward looking optical flow camera<br />
and a front looking stereo camera are the<br />
main sensors.<br />
• All algorithms necessary for autonomous<br />
mapping and exploration run on-board the<br />
MAV.<br />
• Off-board large scale pose-graph SLAM<br />
and loop closure with images transmitted<br />
via Wi-Fi to ground-station.<br />
Visualization of obstacle map and path<br />
planning planning along a corridor<br />
15:15–15:30 WedET1.2<br />
Predicting Micro Air Vehicle Landing Behaviour<br />
from Visual Texture<br />
John Bartholomew, Andrew Calway and Walterio Mayol-Cuevas<br />
Computer Science, University of Bristol, UK<br />
• Motivation: Predicting landing<br />
behaviour enables autonomous<br />
choice of landing site.<br />
• Characteristics of motion during<br />
touch-down on different surfaces are<br />
found experimentally.<br />
• General Regression is used to<br />
predict motion for new surfaces, from<br />
training data.<br />
• We test a known texture descriptor<br />
on challenging imagery from the<br />
MAV.<br />
15:45–16:00 WedET1.4<br />
A Geometrical Approach For Vision Based Attitude And<br />
Altitude Estimation For UAVs In Dark Environments<br />
Ashutosh Natraj 1,3<br />
1 MIS Lab & Le2i Lab, University of Picardie Jules Verne<br />
Peter Sturm 2 , Cedric Demonceaux 3 & Pascal Vasseur 4<br />
2 INRIA Rhone Alpes, Grenoble, France,<br />
3 Le2i Lab, University of Bourgogne, France,<br />
4 Litis Lab, University of Rouen, France.<br />
• We present a single fish-eye<br />
camera-laser projector system on a<br />
fixed base line to estimate altitude &<br />
attitude of UAVs as in fig 1.<br />
• Our system is cheap, light weight<br />
and computationally less expensive<br />
compared over commercial sensors.<br />
Applications:<br />
• Altitude and attitude estimation of<br />
UAVs for vertical take off and landing<br />
(VTOL) and maneuvering in the low<br />
light to dark indoor/outdoor, GPS<br />
deficient unknown environment with<br />
no prebuilt map as shown in figure 2.<br />
<strong>2012</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–161–<br />
Fig 1. Our setup on the UAV-Pelican.<br />
Fig 2. An application in low light-dark<br />
indoor GPS insufficient environment.