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> WedGT1 <strong>Pegaso</strong> A <strong>Wednesday</strong>, <strong>October</strong> <strong>10</strong>, <strong>2012</strong>, 17:30–18:30<br />
Stereo Vision<br />
Chair Il Hong Suh, Hanyang Univ.<br />
Co-Chair Andreas Zell, Univ. of Tübingen<br />
17:30–17:45 WedGT1.1<br />
A New Feature Detector and Stereo Matching<br />
Method for Accurate High-Performance Sparse<br />
Stereo Matching<br />
Konstantin Schauwecker and Andreas Zell<br />
Department Cognitive Systems, University of Tübingen, Germany<br />
Reinhard Klette<br />
Computer Science Department, The University of Auckland, New Zealand<br />
• Computationally efficient sparse stereo<br />
matching system, achieving processing<br />
rates above 200 frames per second on a<br />
commodity dual-core CPU.<br />
• Although features are matched sparsely, a<br />
dense consistency check is applied, which<br />
drastically decreases the number of false<br />
matches.<br />
• A new FAST-based feature detector is<br />
used, which has a less clustered feature<br />
distribution and leads to an improved<br />
matching performance.<br />
18:00–18:15 WedGT1.3<br />
Can Stereo Vision replace a Laser Rangefinder?<br />
M. Antunes, J.P. Barreto, C. Premebida and U. Nunes<br />
Department of Electrical and Computer Engineering<br />
Institute of Systems and Robotics<br />
University of Coimbra, Portugal<br />
• We propose Stereo Rangefinding (SRF)<br />
for estimating depth along virtual scan<br />
planes<br />
• The SymStereo framework is used for<br />
quantifying the likelihood of pixel<br />
correspondences using induced symmetry<br />
• The depth estimates of SRF are compared<br />
against the data provided by a LRF<br />
• We show that passive stereo can be an<br />
alternative to LRF in certain robotic<br />
applications<br />
Our paper shows that it is<br />
possible to recover the profile cut<br />
(green) directly from two cameras<br />
17:45–18:00 WedGT1.2<br />
Real-time Velocity Estimation Based on<br />
Optical Flow and Disparity Matching<br />
Dominik Honegger, Pierre Greisen, Lorenz Meier,<br />
Petri Tanskanen and Marc Pollefeys<br />
ETH Zürich Switzerland<br />
• We present an image-based real-time<br />
metric velocity sensor for mobile robot<br />
navigation.<br />
• An FPGA-based stereo camera platform<br />
combines optical flow and disparity values<br />
at 127 fps and 376*240 resolution.<br />
• Radial undistortion, image rectification,<br />
disparity estimation and optical flow are<br />
performed on a single FPGA.<br />
• Suited for MAVs due to low-weight, lowpower<br />
and low-latency.<br />
<strong>2012</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–184–<br />
System Setup, Rectified Input<br />
Image, Disparity Map,<br />
Flow Field<br />
18:15–18:30 WedGT1.4<br />
Dependable Dense Stereo Matching by Both<br />
Two-layer Recurrent Process and Chaining<br />
Search<br />
Sehyung Lee, Youngbin Park and Il Hong Suh<br />
Department of Electronics and Computer Engineering, Hanyang University,<br />
Korea<br />
• We propose a recurrent two-layer process<br />
and chaining search for dense stereo<br />
matching.<br />
• The disparity map is calculated through<br />
the iterative integration of pixel and region<br />
layers.<br />
• To estimate the precise disparities in<br />
occluded regions, reliable disparities are<br />
propagated by the chaining search.<br />
• To test our algorithm, it was compared<br />
with two outstanding algorithms in<br />
Middlebury benchmark using Gaussian<br />
noisy images.<br />
The first row shows the images with<br />
varying PSNR. The second, third, and<br />
fourth rows show the results of the CVF,<br />
the DBP, and the proposed method.