August 30, 2020 Journal article Open Access

Akash Kuamr Ghanate; Aashish M; Santhosh M Patil, Sowmyarani C N; Sowmyarani C N; Ramakanth Kumar P

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  <identifier identifierType="URL">https://zenodo.org/record/5529579</identifier>
  <creators>
    <creator>
      <creatorName>Akash Kuamr Ghanate</creatorName>
      <affiliation>B.E., Department of Computer Science and  Engineering, R.V College of Engineering, Bangalore, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Aashish M</creatorName>
      <affiliation>B.E., Department of Computer Science and  Engineering, R.V College of Engineering, Bangalore, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Santhosh M Patil, Sowmyarani C N</creatorName>
      <givenName>Sowmyarani C N</givenName>
      <familyName>Santhosh M Patil</familyName>
      <affiliation>B.E., Department of Computer Science and  Engineering, R.V College of Engineering, Bangalore, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Sowmyarani C N</creatorName>
      <affiliation>Associate Professor, Department of Computer  Science and Engineering, R.V College of Engineering, Bangalore, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Ramakanth Kumar P</creatorName>
      <affiliation>Professor &amp; HoD, Department of Computer  Science and Engineering, R.V College of Engineering, Bangalore, India</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Review on 3D Mapping and Segmentation</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>3D Mapping, JSNet, Segmentation, SLAM, Sfm, PointNet</subject>
    <subject subjectScheme="issn">2249-8958</subject>
    <subject subjectScheme="handle">E1020069520/2020©BEIESP</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering  and Sciences Publication(BEIESP)</contributorName>
      <affiliation>Publisher</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2020-08-30</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5529579</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2249-8958</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijeat.E1020.089620</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;The deployment of a robot in a remote environment is a field of research that has huge applications. The robotic system must have the capability of sensing its surroundings and being aware of what it is around. We concluded two key tasks for this purpose, which are 3D mapping and segmentation. This paper shows a comprehensive review of the different 3D mapping and segmentation methods. Mapping techniques include those using RGB images, RGBD images and LIDAR. Segmentation techniques include PointNet, PointNet++, 3D semantic and instance segmentation and joint instance segmentation. We also describe two end-to-end approaches for mapping and segmentation. These methods are reviewed elaborately, comparisons are drawn between them, challenges are presented and future directions in addressing these challenges are pointed out.&lt;/p&gt;</description>
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