Dataset Open Access

UnLoc dataset (Synthetic + Real)

Loing, Vianney


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  <identifier identifierType="DOI">10.5281/zenodo.2563622</identifier>
  <creators>
    <creator>
      <creatorName>Loing, Vianney</creatorName>
      <givenName>Vianney</givenName>
      <familyName>Loing</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-4802-8208</nameIdentifier>
      <affiliation>Ecole des Ponts ParisTech</affiliation>
    </creator>
  </creators>
  <titles>
    <title>UnLoc dataset (Synthetic + Real)</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>uncalibrated relative localization</subject>
    <subject>pose estimation</subject>
    <subject>synthetic data</subject>
    <subject>virtual training</subject>
    <subject>robotics</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-02-12</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2563622</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsSupplementedBy">10.1007/s11263-018-1102-6</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2563621</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This dataset contains the synthetic and real data used in the article &amp;quot; Virtual Training for a Real Application: Accurate Object-Robot Relative Localization Without Calibration &amp;quot; to train 3 convolutional neural networks (CNNs) in order to perform uncalibrated relative localization of a cuboid block with respect to a robot, and to evaluate them.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;It consists of a dataset composed of synthetic pictures for training the CNNs and a dataset of real pictures for evaluation.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The &amp;quot;synthetic&amp;quot; dataset is composed 3 sub-datasets (each of them composed of thousands of synthetic pictures and corresponding groundtruth) for training :&amp;nbsp;&lt;/p&gt;

&lt;ol&gt;
	&lt;li&gt;a dataset for coarse relative localization subtask : coarse_estimation_data (~14.6 GB when extracted)&lt;/li&gt;
	&lt;li&gt;a dataset for the tool localization subtask :&amp;nbsp;tool_detection_data&amp;nbsp;(~4.3&amp;nbsp;GB when extracted)&lt;/li&gt;
	&lt;li&gt;a dataset for the fine relative localization subtask :&amp;nbsp;fine_estimation_data&amp;nbsp;(~13.3 GB when extracted)&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;These sub-datasets are composed of raw data as well as post-treated data ready to be used for training CNNs,&amp;nbsp;in CSV format and in Torch format (.t7).&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The &amp;quot;real&amp;quot; dataset is&amp;nbsp;composed of real pictures&amp;nbsp;with a precisely localized cuboid block for evaluation only : UnLoc_real (~2.5&amp;nbsp;GB when extracted).&amp;nbsp;&lt;/p&gt;

&lt;p&gt;More information available on the project page :&amp;nbsp;&lt;a href="http://imagine.enpc.fr/~loingvi/unloc/"&gt;http://imagine.enpc.fr/~loingvi/unloc/&lt;/a&gt;&lt;/p&gt;</description>
    <description descriptionType="Other">{"references": ["Loing et al. (2018). Dataset associated to arXiv:1902.02711"]}</description>
  </descriptions>
</resource>
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