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maxmixture.html
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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<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">
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<center>
<img src="openslam-logo.png" border=0>
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<br>
<center><h2>Max-Mixture</h2></center><center><table width=700> <tr><tr><td colspan=2>
Max-mixture allows handling large number of outliers and
multimodal constraints in the least square SLAM formulation. The
code here is implemented as a plugin for g2o.
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<br><b>Authors</b><br>
<a href="http://www.informatik.uni-freiburg.de/~agarwal" target="_blank">Pratik Agarwal</a>;
<a href="http://april.eecs.umich.edu/people/ebolson/" target="_blank">Edwin Olson</a>;
<a href="http://www.informatik.uni-freiburg.de/~burgard" target="_blank">Wolfram Burgard</a>;
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<br><b><a target="_blank" href="https://github.com/OpenSLAM-org/openslam_maxmixture">Get the Source Code!</a><br>
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<br> <b>Long Description</b><br>
The central challenge in robotic mapping is obtaining reliable data
associations (or loop closures): state-of-the-art inference
algorithms can fail catastrophically if even one erroneous loop
closure is incorporated into the map. Consequently, much work has
been done to push error rates closer to zero. However, a long-lived
or multi-robot system will still encounter errors, leading to
system failure. We propose a fundamentally different approach:
allow richer error models that allow the probability of a failure
to be explicitly modeled. In other words, we optimize the map while
simultaneously determining which loop closures are correct from
within a single integrated Bayesian framework. Unlike earlier
multiple-hypothesis approaches, our approach avoids exponential
memory complexity and is fast enough for real-time performance. Our
method not only allows loop closing errors to be automatically
identified, but also that in extreme cases, the front-end
loop-validation systems can be unnecessary.
The package contains the code and a plugin for g2o with some sample datasets.
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<br> <b> Hardware/Software Requirements</b><br>
All requirements of g2o.
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<br> <b>Papers Describing the Approach</b>
<br> Edwin Olson and Pratik Agarwal:
Inference on networks of mixtures for robust robot mapping,
Proceedings of Robotics: Science and Systems, 2012 (<a href="http://roboticsproceedings.org/rss08/p40.html" target="_blank">link</a>)<br>
<br> Edwin Olson and Pratik Agarwal:
Inference on networks of mixtures for robust robot mapping,
Internation Journal of Robotics Research (To Appear), 2013<br>
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<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><b>Further Links</b>
<br><a href="https://github.com/agpratik/max-mixture" target="_blank"> Development code is available at max-mixture on Github</a>
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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://www.informatik.uni-freiburg.de/~agarwal" target="_blank">Pratik Agarwal</a>;
<a href="http://april.eecs.umich.edu/people/ebolson/" target="_blank">Edwin Olson</a>;
<a href="http://www.informatik.uni-freiburg.de/~burgard" target="_blank">Wolfram Burgard</a>;
*** <br>
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