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1-Point
RANSAC Inverse Depth EKF Monocular
SLAM
Matlab
Code. Version 1.01 |
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This
code performs EKF Structure from Motion /
SLAM from a monocular sequence. That is, taking as the only input an
image sequence with known camera calibration, it estimates the 6
degrees-of-freedom camera motion and a sparse 3D map of point features
using the Extended Kalman Filter.
Point features are coded using inverse depth. Spurious rejection is
efficiently solved by a novel 1-point RANSAC algorithm. Find all the
details here. |
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LICENSE |
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This
code is released as free software: you can redistribute
it and/or modify it under the terms of the GNU General Public License
as published by the Free Software Foundation. Read http://www.gnu.org/copyleft/gpl.html. |
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If
you use this code for
academic work, please reference [1] |
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[1] |
Javier
Civera, Óscar G. Grasa, Andrew J.
Davison, J. M. M. Montiel
1-Point RANSAC for EKF Filtering:
Application to Real-Time Structure
from Motion and Visual Odometry (draft)
(video: monocular+odometry)
Journal of Field
Robotics, vol. 27(5), pp. 609-631, October 2010. |
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DOWNLOAD |
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Click
here. (Version 1.01) |
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IMPORTANT: In this Matlab code, we are using a camera model that unfortunately does not match the one in the popular Matlab Calibration Toolbox. Check the details in the appendix of our TRO2008.
For using our code, you might need to convert from the Matlab
Calibration Toolbox to the model we use. You can download a
quick-and-dirty Matlab code to do that here. It has been written and used by people in our lab (Oscar G. Grasa, J. M. M. Montiel and me basically). I do not have much time to clean it, I hope it is of use in its raw state. |
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QUICK
START |
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1)
Download and unzip.
2) Go to matlab_code directory. The directory sequences
contains
an example sequence.
3) In Matlab, run mono_slam.m. The code will run using the
example sequence. It should show something like this. |
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CONTACT |
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Questions,
comments, suggestions, discussion and
bug reports are welcomed.
Please, mail to jcivera@unizar.es |
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RELEASE HISTORY |
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22/February/2013.
Version 1.01 released. Fixes a bug, in the previous version the
function ransac_hypotheses was trying more hypotheses than the strictly
necessary. Thanks to Joakim Hugmark for reporting the bug. |