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> WedGT6 Gemini 3 <strong>Wednesday</strong>, <strong>October</strong> <strong>10</strong>, <strong>2012</strong>, 17:30–18:30<br />
Localization and Mapping III<br />
Chair Jun Miura, Toyohashi Univ. of Tech.<br />
Co-Chair<br />
17:30–17:45 WedGT6.1<br />
Efficient Search for Correct and Useful<br />
Topological Maps<br />
Collin Johnson and Benjamin Kuipers<br />
EECS, University of Michigan, USA<br />
• We present an algorithm for probabilistic<br />
topological mapping.<br />
• Perform a heuristic search of a tree of<br />
maps.<br />
• Runs online.<br />
• Never prunes consistent topological map<br />
hypotheses so correct map can always be<br />
found.<br />
Top: Topological map built by our<br />
algorithm<br />
Bottom: Hypotheses expanded<br />
by our algorithm vs. brute force<br />
18:00–18:15 WedGT6.3<br />
Accurate 3D maps from depth images and<br />
motion sensors via nonlinear Kalman filtering<br />
Thibault Hervier, Silvère Bonnabel, François Goulette<br />
Centre de Robotique - CAOR, MINES ParisTech, France<br />
• Use of depth images as localization<br />
sensors<br />
• Combined with ICP<br />
• Analysis of ICP results<br />
• Data fusion with non-linear filtering:<br />
Invariant Extended Kalman Filter<br />
• Natural, robust, handles SE3<br />
• Experiments with Kinect sensor and<br />
gyros shows improvement in accuracy<br />
of localization and map building<br />
Depth images<br />
ICP<br />
Localization<br />
& covariance<br />
Non linear Kalman<br />
filtering (IEKF)<br />
3D maps<br />
Experimental setup<br />
Motion data<br />
(gyros)<br />
17:45–18:00 WedGT6.2<br />
Accurate On-Line 3D Occupancy Grids<br />
Using Manhattan World Constraints<br />
Brian Peasley and Stan Birchfield<br />
Electrical and Computer Engineering Dept, Clemson University, USA<br />
Alex Cunningham and Frank Dellaert<br />
School of Interactive Computing, Georgia Institute of Technology, USA<br />
• Large dense 3D occupancy grids<br />
are constructed from RGB-D data<br />
• Factor graphs are used to combine<br />
odometry and visual data<br />
constrained by a Manhattan World<br />
assumption<br />
• Manhattan World assumption<br />
removes rotational drift – no need<br />
for loop closure<br />
• Large 3D maps of environments<br />
efficiently stored using an octree<br />
<strong>2012</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–188–<br />
3D and 2D reconstruction of<br />
a large building environment<br />
18:15–18:30 WedGT6.4<br />
Fourier-based Registrations for Two-Dimensional<br />
Forward-Looking Sonar Image Mosaicing<br />
Natalia Hurtos, Xavier Cufí and Joaquim Salvi<br />
Computer Vision and Robotics Group, University of Girona, Spain<br />
Yvan Petillot<br />
Ocean Systems Laboratory, Heriot -Watt University, U.K.<br />
• Phase correlation method is used to<br />
address the registration of forward-looking<br />
sonar images.<br />
• Registrations from loop-closing situations<br />
and areas without abundant features are<br />
feasible.<br />
• By integrating the result of pairwise<br />
registrations into a pose-based graph<br />
optimization a consistent sonar mosaic is<br />
generated.<br />
• The vehicle motion in x,y and heading can<br />
be also estimated from the registrations.