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> WedBVT6 Gemini 3 <strong>Wednesday</strong>, <strong>October</strong> <strong>10</strong>, <strong>2012</strong>, 09:30–<strong>10</strong>:30<br />
Mapping II<br />
Chair Edwin Olson, Univ. of Michigan<br />
Co-Chair<br />
09:30–09:45 WedBVT6.1<br />
Variable reordering strategies for SLAM<br />
• We have evaluated existing<br />
reordering strategies on standard<br />
SLAM datasets.<br />
Pratik Agarwal and Edwin Olson<br />
Computer Science and Engineering,<br />
University of Michigan, USA<br />
• We propose an easy to implement<br />
reordering algorithm called BHAMD<br />
which yields competitive performance.<br />
• We provide evidence showing that<br />
few gains remain with respect to<br />
variants of minimum degree ordering.<br />
Reorder and solve time for<br />
different reordering algorithms<br />
<strong>10</strong>:00–<strong>10</strong>:15 WedBVT6.3<br />
Planar Polygon Extraction and Merging from<br />
Depth Images<br />
Joydeep Biswas and Manuela Veloso<br />
School of Computer Science, Carnegie Mellon University, USA<br />
We introduce an approach to building 3D<br />
maps of indoor environments modelled as<br />
planar surfaces using depth cameras.<br />
•Neighborhoods of plane filtered points are<br />
extracted from each observed depth image<br />
and fitted to convex polygons.<br />
•The polygons from each frame are<br />
matched to existing map polygons using<br />
OpenGL accelerated ray casting.<br />
•Matched polygons are then merged over<br />
time using sequential update and merging of<br />
the scatter matrix of observed polygons.<br />
The polygon extraction and merging<br />
algorithms take on an average 2.5 ms for<br />
each depth image of size 640x480 pixels.<br />
Plane Filtered and fitted convex polygons<br />
shown in blue from each frame (top) are<br />
merged across successive frames to<br />
generate complete map (bottom).<br />
<strong>10</strong>:20–<strong>10</strong>:25 WedBVT6.5<br />
2D PCA-based Localization for Mobile Robots<br />
in Unstructured Environments<br />
Fernando Carreira (1) , Camilo Christo (2) , Duarte Valério (2) ,<br />
Mário Ramalho (2) , Carlos Cardeira (2) , João Calado (1,2)<br />
and Paulo Oliveira (3)<br />
(1) ADEM / ISEL, Polytechnic Institute of Lisbon, Portugal<br />
(2) IDMEC / IST, Technical University of Lisbon, Portugal<br />
(3) ISR / IST, Technical University of Lisbon, Portugal<br />
• Self-localization system for mobile<br />
robots to operate in indoor<br />
environment, with only onboard<br />
sensors<br />
• The database of images stored<br />
onboard is of reduced size, when<br />
compared with acquired images<br />
• No hypothesis is made about specific<br />
features in the environment<br />
• The localization system estimates in<br />
real time the position and slippage<br />
with global stable error dynamics.<br />
Results of stability tests considering<br />
wrong initial position and attitude<br />
09:45–<strong>10</strong>:00 WedBVT6.2<br />
An Object-Based Semantic World Model for Long-<br />
Term Change Detection and Semantic Querying<br />
• RGB-D based mapping<br />
aboard a localized robot<br />
• Uniquely weak perceptual<br />
assumptions<br />
• Scales to large (1600 m 2 ),<br />
long-term (six weeks)<br />
operation<br />
• Supports change detection<br />
and semantic querying<br />
• 326 GB dataset available!<br />
Julian Mason and Bhaskara Marthi<br />
Duke University and Willow Garage<br />
<strong>2012</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–138–<br />
Example of semantic query for<br />
“medium-sized things in the cafeteria.”<br />
<strong>10</strong>:15–<strong>10</strong>:20 WedBVT6.4<br />
Reconfigurable Intelligent Space, R+iSpace,<br />
and Mobile Module, MoMo<br />
JongSeung Park<br />
Graduate School of Sci. and Eng, Ritsumeikan University, Japan<br />
Joo-Ho Lee<br />
College of Information Sci. and Eng, Ritsumeikan University, Japan<br />
• In this video and paper, the new concept<br />
of Intelligent space, ‘R+iSpace’ is<br />
introduced.<br />
• The whole devices in the R+iSpace can<br />
rearrange themselves automatically<br />
according to situation.<br />
• To achieve R+iSpace in real environment,<br />
we propose a mobile module, ‘MoMo’.<br />
• The devices can move on the wall and the<br />
ceiling through mounting on the MoMo.<br />
The R+iSpace system<br />
and The MoMo<br />
<strong>10</strong>:25–<strong>10</strong>:30 WedBVT6.6<br />
Deformable Soft Wheel Robot using<br />
Hybrid Actuation<br />
Je-sung Koh, Dae-young Lee, Seung-won Kim<br />
and Kyu-jin Cho<br />
Dept. Mechanical and Aerospace Eng., Seoul National University, South Korea<br />
• Multimodal motion ; Three driving modes<br />
for penetrating obstacles.<br />
• Caterpillar motion : passing through<br />
the gap<br />
• Wheel driving : fast movement on the<br />
ground<br />
• Legged wheel motion : climbing the<br />
stair<br />
• Hybrid actuation system<br />
• Deformable wheel : Shape memory<br />
alloy coil spring actuator<br />
• Wheel driving : DC motor<br />
Three driving modes of<br />
Deformable wheel robot