Conference paper Open Access

Localization using Dual Fail/Safe Filters with Sensor Fusion in Complex Urban Environments

Park, Munsu; Hwang, Sung-Ho


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  <identifier identifierType="DOI">10.5281/zenodo.4062844</identifier>
  <creators>
    <creator>
      <creatorName>Park, Munsu</creatorName>
      <givenName>Munsu</givenName>
      <familyName>Park</familyName>
    </creator>
    <creator>
      <creatorName>Hwang, Sung-Ho</creatorName>
      <givenName>Sung-Ho</givenName>
      <familyName>Hwang</familyName>
    </creator>
  </creators>
  <titles>
    <title>Localization using Dual Fail/Safe Filters with Sensor Fusion in Complex Urban Environments</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Autonomous vehicle</subject>
    <subject>Sensor Fusion</subject>
    <subject>HD Map</subject>
    <subject>Localization</subject>
    <subject>Complex Urban Environment</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-10-02</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4062844</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4062843</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/evs33</relatedIdentifier>
  </relatedIdentifiers>
  <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;It is difficult to obtain reliable data without proper calibration because variations in illumination and surroundings in complex urban environments affect the sensor input. This study proposes a localization algorithm using sensor fusion in complex urban environments, that applies a fail/safe filter to improve the reliability of data obtained from sensors that reduce dependence on the characteristics of the surrounding environments. This study proposes a sensor fusion localization algorithm in complex urban environments that applies a fail/safe filter to improve the reliability of data obtained from the sensors that are less affected by the characteristics of the surrounding environments. LiDAR reflections provide data that is unaffected by illumination changes, and can be used for sensor fusion by calibrating in-vehicle sensors rather than expensive IMUs. The fail/safe filter compares the curvature of the lane or the distance traveled, and determines the boundary point using the rate of change of the sensor and vehicle model. The boundary points and position data are compared to determine the reliability of the data. The performance of the proposed filter was verified by applying it to a real vehicle in K-City and Suseong Alpha City.&lt;/p&gt;</description>
  </descriptions>
</resource>
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