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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html>
<head>
<title>OpenSLAM.org</title>
<meta name="description" content="OpenSLAM.org">
<meta name="keywords" content="OpenSLAM SLAM robot mapping localization research">
<meta http-equiv="content-type" content="text/html;charset=ISO-8859-1">
<meta name="robots" content="index">
<meta http-equiv="expires" content="Sat, 01 Dec 2001 00:00:00 GMT">
<link rel="stylesheet" type="text/css" href="style2.css">
</head>
<body bgcolor="#FFFFFF">
<br>
<br>
<center>
<img src="openslam-logo.png" border=0>
</center>
<br>
<center><h2>GridSLAM</h2></center><center><table width=700> <tr><tr><td colspan=2>
GridSLAM is an easy to use and understand Rao-Blackwellized particle filer to learn
grid maps from laser range data.
<br><a href="http://www.informatik.uni-freiburg.de/~haehnel/old/research/scan-matching-fastslam/index.html" target="_blank">Further information</a>
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<br><b>Authors</b><br>
<a href="http://seattle.intel-research.net/people/dhaehnel/" target="_blank">Dirk Haehnel</a>;
<a href="http://www.cs.washington.edu/homes/fox/" target="_blank">Dieter Fox</a>;
<a href="http://www.informatik.uni-freiburg.de/~burgard" target="_blank">Wolfram Burgard</a>;
<a href="http://robots.stanford.edu" target="_blank">Sebastian Thrun</a>;
</td></tr>
<tr><td colspan=2>
<br><b><a target="_blank" href="https://github.com/OpenSLAM-org/openslam_gridslam">Get the Source Code!</a><br>
</td></tr>
<tr><td colspan=2>
<br> <b>Long Description</b><br>
The ability to learn a consistent model of its environment is
a prerequisite for autonomous mobile robots. A particularly
challenging problem in acquiring environment maps is that of
closing loops; loops in the environment create challenging
data association problems. This work presents a novel
algorithm that combines Rao-Blackwellized particle filtering
and scan matching. In our approach scan matching is used for
minimizing odometric errors during mapping. A probabilistic
model of the residual errors of scan matching process is then
used for the resampling steps. This way the number of samples
required is seriously reduced. Simultaneously we reduce the
particle depletion problem that typically prevents the robot
from closing large loops. We present extensive experiments
that illustrate the superior performance of our approach
compared to previous approaches.
<br><br></td></tr>
<tr><td colspan=2>
<br> <b>Input Data </b><br> The approach takes raw laser range data and
(scan-matched) odometry.
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<tr><td colspan=2>
<br> <b>Logfile Format</b><br>
Carmen log format as well as Dirk Haehnel's rec format
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<tr><td colspan=2>
<br> <b>Type of Map</b><br> grid maps
</td></tr>
<tr><td colspan=2>
<br> <b> Hardware/Software Requirements</b><br>
Linux/Unix, GCC 3.3/4.x
<br>
CARMEN
<br>
Qt
<br>
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<tr><td colspan=2>
<br> <b>Papers Describing the Approach</b>
<br> D. Haehnel, D. Fox, W. Burgard, S. Thrun:
A highly efficient FastSLAM algorithm for generating cyclic maps of large-scale environments from raw laser range measurements,
In Proc. of the Conference on Intelligent Robots and Systems (IROS), 2003 (<a href="http://www.informatik.uni-freiburg.de/~haehnel/old/papers/haehnel.iros03.pdf" target="_blank">link</a>)<br>
</td></tr>
<tr><td colspan=2>
<br><b>License Information</b><br>
This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.<br>
The authors allow the users of OpenSLAM.org to use and modify the source code for their own research. Any commercial application, redistribution, etc has to be arranged between users and authors individually and is not covered by OpenSLAM.org.<br><br>
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<br><br>
*** OpenSLAM.org is not responsible for the content of this webpage *** <br>
*** Copyright and V.i.S.d.P.:
<a href="http://seattle.intel-research.net/people/dhaehnel/" target="_blank">Dirk Haehnel</a>;
<a href="http://www.cs.washington.edu/homes/fox/" target="_blank">Dieter Fox</a>;
<a href="http://www.informatik.uni-freiburg.de/~burgard" target="_blank">Wolfram Burgard</a>;
<a href="http://robots.stanford.edu" target="_blank">Sebastian Thrun</a>;
*** <br>
</td></tr>
</table></center>
</body>
</html>