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    <id>3315</id>
    <completedYear>2024</completedYear>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>774</pageFirst>
    <pageLast>781</pageLast>
    <pageNumber>8</pageNumber>
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    <type>conferenceobject</type>
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    <title language="deu">RIV-SLAM: Radar-Inertial-Velocity optimization based graph SLAM</title>
    <abstract language="eng">4D imaging radars, commonly known as 4D radars, deliver comprehensive point cloud data that encapsulates range, azimuth, elevation, and Doppler velocity information even in harsh environmental conditions, such as&#13;
rain, snow, smoke, and fog. However, 4D radar data also suffers from high noise and sparsity, which poses great challenges for SLAM applications. This paper presents RIV-SLAM, a complete radar-inertial-velocity optimization-based graph SLAM system designed to exploit the full potential of 4D imaging radar technology. RIV-SLAM consists of four integral components: front-end, loop closure, IMU pre-integration and&#13;
graph optimization, each optimized to effectively leverage the unique attributes of radar data and tightly coupled with IMU data. This is also the first SLAM system known to us that outputs an optimized ego velocity. This capability ensures reliable ego motion estimation under extreme conditions (e.g., wheel odometry fails). Furthermore, we develop a new&#13;
ground extraction approach, specifically adapted for the 4D imaging radar, which substantially improves the system’s z-axis accuracy. Comprehensive evaluations of the RIV-SLAM system on a variety of datasets demonstrate its superior performance, significantly surpassing existing state-of-the-art Radar-SLAM frameworks. The code of RIV-SLAM will be released at: RIV-&#13;
SLAM</abstract>
    <identifier type="doi">10.1109/CASE59546.2024.10711511</identifier>
    <enrichment key="conference_title">2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)</enrichment>
    <enrichment key="conference_place">Bari, Italy</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>Dong Wang</author>
    <author>Stefan May</author>
    <author>Andreas Nüchter</author>
    <collection role="institutes" number="">Fakultät für Elektrotechnik Feinwerktechnik Informationstechnik</collection>
    <collection role="Forschungsschwerpunkt" number="5">Digitalisierung &amp; Künstliche Intelligenz</collection>
  </doc>
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