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

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