Dataset

OpenSeqSLAM source code

Queensland University of Technology
Niko Sünderhauf (Managed by)
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://openslam.org/&rft.title=OpenSeqSLAM source code&rft.identifier=10378.3/8085/1018.15614&rft.publisher=Queensland University of Technology&rft.description=OpenSeqSLAM is an open source Matlab implementation of the original SeqSLAM algorithm published by Michael Milford and Grodon Wyeth at the 2012 IEEE International Conference on Robotics and Automation (ICRA12). SeqSLAM performs place recognition by matching sequences of images as opposed to matching single images such as FAB-MAP does. SeqSLAM has achieved remarkable results recognizing places even if the environment underwent severe appearance changes, like transitioning from a sunny day to a rainy night. In a preprocessing step, SeqSLAM drastically downsamples incoming images to e.g. 64x32 pixels. These thumbnail images are further divided into patches of 8x8 pixels, which are then normalized, so that the pixel values cover the complete range of possible values between 0 and 255. The two innovative steps of SeqSLAM perform as follows: First, the distance matrix is locally contrast enhanced, which Milford and Wyeth describe as a step towards forcing the matcher to find best matches in every local neighborhood of the trajectory instead of only one global best match. Finally, when looking for a match to a query image, SeqSLAM performs a search to find the best matching sequence of adjacent frames. SeqSLAM literally sweeps through the contrast-enhanced difference matrix to achieve this. &rft.creator=Anonymous&rft.date=2014&rft_rights=Copyright and V.i.S.d.P.: Niko Suenderhauf https://openslam.org/&rft_rights=Creative Commons Attribution-NonCommercial 3.0 http://creativecommons.org/licenses/by-nc/3.0/au/&rft_subject=Information and Computing Sciences&rft.type=dataset&rft.language=English Go to Data Providers

Licence & Rights:

Non-Commercial Licence view details
CC-BY-NC

Creative Commons Attribution-NonCommercial 3.0
http://creativecommons.org/licenses/by-nc/3.0/au/

Copyright and V.i.S.d.P.: Niko Suenderhauf
https://openslam.org/

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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.
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.

OpenSeqSLAM is licenced under GPL 3.0.

Contact Information

Ph: +61 7 3138 9971
Dr Niko Suenderhauf

niko.suenderhauf@qut.edu.au



Full description

OpenSeqSLAM is an open source Matlab implementation of the original SeqSLAM algorithm published by Michael Milford and Grodon Wyeth at the 2012 IEEE International Conference on Robotics and Automation (ICRA12).

SeqSLAM performs place recognition by matching sequences of images as opposed to matching single images such as FAB-MAP does. SeqSLAM has achieved remarkable results recognizing places even if the environment underwent severe appearance changes, like transitioning from a sunny day to a rainy night.

In a preprocessing step, SeqSLAM drastically downsamples incoming images to e.g. 64x32 pixels. These thumbnail images are further divided into patches of 8x8 pixels, which are then normalized, so that the pixel values cover the complete range of possible values between 0 and 255.

The two innovative steps of SeqSLAM perform as follows: First, the distance matrix is locally contrast enhanced, which Milford and Wyeth describe as a step towards forcing the matcher to find best matches in every local neighborhood of the trajectory instead of only one global best match. Finally, when looking for a match to a query image, SeqSLAM performs a search to find the best matching sequence of adjacent frames. SeqSLAM literally sweeps through the contrast-enhanced difference matrix to achieve this.

Data time period: 12 2011 to 12 2013

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Identifiers
  • Local : 10378.3/8085/1018.15614