<?xml version="1.0" encoding="UTF-8"?>
<article xmlns:xlink="http://www.w3.org/1999/xlink" xml:lang="en" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <front>
    <journal-meta>
      <journal-id journal-id-type="ojs">JDC</journal-id>
      <journal-title-group>
        <journal-title xml:lang="en">Journal of Discovery Core</journal-title>
        <abbrev-journal-title xml:lang="en">JDC</abbrev-journal-title>
      </journal-title-group>
      <publisher>
        <publisher-name>Digital Intelligence Press</publisher-name>
        <publisher-loc>Hong Kong<country>HK</country><uri>https://dipscie.com/index.php/JDC/index</uri></publisher-loc>
      </publisher>
      <issn pub-type="epub">3135-7687</issn>
      <self-uri xlink:href="https://dipscie.com/index.php/JDC"/>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">148</article-id>
      <article-categories>
        <subj-group xml:lang="en" subj-group-type="heading">
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title xml:lang="en">&lt;bold&gt;Deep Learning Based Dynamic Object Filtering for Semantic Information Assisted Visual LiDAR SLAM in Dynamic Scenes&lt;/bold&gt;</article-title>
      </title-group>
      <contrib-group content-type="author">
        <contrib>
          <name-alternatives>
            <name name-style="western" specific-use="primary">
              <surname>Zhao</surname>
              <given-names>Xianlong</given-names>
            </name>
          </name-alternatives>
          <email>zxl041108@126.com</email>
          <xref ref-type="aff" rid="aff-1"/>
        </contrib>
      </contrib-group>
      <aff id="aff-1">
        <institution content-type="orgname">Swinburne College, Shandong University of Science and Technology, Jinan, Shandong, China</institution>
      </aff>
      <pub-date date-type="pub" publication-format="epub">
        <day>30</day>
        <month>10</month>
        <year>2026</year>
      </pub-date>
      <fpage>136</fpage>
      <lpage>163</lpage>
      <pub-history>
        <event event-type="received">
          <event-desc>Received: <date date-type="received" iso-8601-date="2026-08-13T10:03:45+00:00"><day>13</day><month>8</month><year>2026</year></date></event-desc>
        </event>
      </pub-history>
      <permissions>
        <copyright-statement>Copyright (c) 2026 Xianlong Zhao (Author)</copyright-statement>
        <copyright-year>2026</copyright-year>
        <copyright-holder>Xianlong Zhao (Author)</copyright-holder>
        <license xlink:href="https://creativecommons.org/licenses/by/4.0">
          <license-p>&lt;a rel="license" href="https://creativecommons.org/licenses/by/4.0/"&gt;&lt;img alt="Creative Commons License" src="//i.creativecommons.org/l/by/4.0/88x31.png" /&gt;&lt;/a&gt;&lt;p&gt;This work is licensed under a &lt;a rel="license" href="https://creativecommons.org/licenses/by/4.0/"&gt;Creative Commons Attribution 4.0 International License&lt;/a&gt;.&lt;/p&gt;</license-p>
        </license>
      </permissions>
      <self-uri xlink:href="https://dipscie.com/index.php/JDC/article/view/10.67541_jdc2612"/>
      <kwd-group xml:lang="en">
        <kwd>Visual-LiDAR SLAM; Dynamic object filtering; Evidence arbitration; Semantic modulation; Tightly coupled optimization</kwd>
      </kwd-group>
      <counts>
        <page-count count="28"/>
      </counts>
      <custom-meta-group/>
    </article-meta>
  </front>
  <body/>
  <back>
    <ref-list>
      <ref id="R1">
        <mixed-citation>[1] Wang, H., Hu, Q., Li, B., Geng, Y., Su, C., Zhu, S., ... &amp; Chen, W. (2026). RTS-SLAM: A Trajectory Consistency-Driven Multi-Constraint Dynamic Feature-Rejection Method for Visual SLAM in Dynamic Environments. Sensors, 26(9), 2846. https://doi.org/10.3390/s26092846</mixed-citation>
      </ref>
      <ref id="R2">
        <mixed-citation>[2] Polukhin, A., Stirenko, S., Leier, M., Jervan, G., Rokovyi, O., Alienin, O., ... &amp; Gordienko, Y. (2026). Depth-assisted sparse visual odometry for UAV-relevant synthetic RGB-D evaluation: A controlled geometric-backend ablation. Robotics, 15(7), 128. https://doi.org/10.3390/robotics15070128</mixed-citation>
      </ref>
      <ref id="R3">
        <mixed-citation>[3] Zheng, S., Li, J., Wang, G., Ni, M., Palit, A., Montana, G., &amp; Guan, Y. (2026). Person parametric physics-informed representation for mmwave-based human pose estimation. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 10(2), 1-34. https://doi.org/10.1145/3810219</mixed-citation>
      </ref>
      <ref id="R4">
        <mixed-citation>[4] Zhang, Y. X., Gui, J., Yu, B., Cong, X., Gong, X., Tao, W., &amp; Tao, D. (2026). Deep learning-based point cloud registration: A comprehensive survey and taxonomy. International Journal of Computer Vision, 134(4), 168. https://doi.org/10.1007/s11263-025-02723-w</mixed-citation>
      </ref>
      <ref id="R5">
        <mixed-citation>[5] Cheng, X., Liu, X., Li, J., &amp; Zhou, W. (2024). Deep learning-based point cloud registration: a comprehensive investigation. International Journal of Remote Sensing, 45(10), 3412-3442. https://doi.org/10.1080/01431161.2024.2343434</mixed-citation>
      </ref>
      <ref id="R6">
        <mixed-citation>[6] Liu, M., Zou, Q., Long, J., Wang, Y., Lin, M., &amp; Wang, F. (2025). SSE-SLAM: Semantic Visual SLAM Based on RGB-D Camera for High-Accuracy Pose Measurement in Dynamic Environments. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/tim.2025.3621768</mixed-citation>
      </ref>
      <ref id="R7">
        <mixed-citation>[7] Zhou, T., Porikli, F., Crandall, D. J., Van Gool, L., &amp; Wang, W. (2022). A survey on deep learning technique for video segmentation. IEEE transactions on pattern analysis and machine intelligence, 45(6), 7099-7122. https://doi.org/10.1109/TPAMI.2022.3225573</mixed-citation>
      </ref>
      <ref id="R8">
        <mixed-citation>[8] Li, X., Wang, L., Luo, H., &amp; Li, G. (2026). RI-DVP: A Physics–Geometry Dual-Driven Framework for Static Map Construction in Sparse LiDAR Scenarios. Remote Sensing, 18(5), 821. https://doi.org/10.3390/rs18050821</mixed-citation>
      </ref>
      <ref id="R9">
        <mixed-citation>[9] Chen, Z., Zhang, F., Zhu, C., Zhang, H., Chen, L., Zhu, H., &amp; Yu, F. (2026). Enhancing PointPillars for 3-D Small Object Detection: A Multi-Dimensional Feature Fusion Approach. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2026.3680151</mixed-citation>
      </ref>
      <ref id="R10">
        <mixed-citation>[10] Kojukhov, A., Levin, I., &amp; Bovshover, A. (2025). From Subsumption to Semantic Mediation: A Generative Orchestration Architecture for Autonomous Systems. Algorithms, 18(12), 773. https://doi.org/10.3390/a18120773</mixed-citation>
      </ref>
      <ref id="R11">
        <mixed-citation>[11] Sanket, N. J., Singh, C. D., Fermüller, C., &amp; Aloimonos, Y. (2023). Ajna: Generalized deep uncertainty for minimal perception on parsimonious robots. Science Robotics, 8(81), eadd5139. https://doi.org/10.1126/scirobotics.add5139</mixed-citation>
      </ref>
      <ref id="R12">
        <mixed-citation>[12] Sun, Z., Xu, W., Shi, L., Cen, Y., Jin, Y., &amp; Li, Y. (2026). UAGM: Uncertainty-Aware Geometric Modeling for Multi-Scenario 3-D Object Detection in Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2026.3685623</mixed-citation>
      </ref>
      <ref id="R13">
        <mixed-citation>[13] Fang, Z., Zhou, S., Cui, Y., &amp; Scherer, S. (2020). 3d-siamrpn: An end-to-end learning method for real-time 3d single object tracking using raw point cloud. IEEE Sensors Journal, 21(4), 4995-5011. https://doi.org/10.1109/jsen.2020.3033034</mixed-citation>
      </ref>
      <ref id="R14">
        <mixed-citation>[14] Che, R., Hu, H., Wang, N., &amp; Li, Z. (2026). Dense SLAM using 3D Gaussian Splatting with IMU and Geometric Constraints. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2026.3662499</mixed-citation>
      </ref>
      <ref id="R15">
        <mixed-citation>[15] Tan, Z., Zhang, Z., Lyu, Y., &amp; Pan, Q. (2026). EDOG-Map: Evidential Dynamic Occupancy Grid Mapping for Consumer-Grade UAV Navigation in Dynamic Environments. IEEE Transactions on Consumer Electronics. https://doi.org/10.1109/tce.2026.3725136</mixed-citation>
      </ref>
      <ref id="R16">
        <mixed-citation>[16] Hirt, P. R., Holtkamp, J., Hoegner, L., Xu, Y., &amp; Stilla, U. (2022). Occlusion detection of traffic signs by voxel-based ray tracing using highly detailed models and MLS point clouds of vegetation. International Journal of Applied Earth Observation and Geoinformation, 114, 103017. https://doi.org/10.1016/j.jag.2022.103017</mixed-citation>
      </ref>
      <ref id="R17">
        <mixed-citation>[17] Chakraborty, D., &amp; Dey, E. K. (2024). Segmentation of LiDAR point cloud data in urban areas using adaptive neighborhood selection technique. Plos one, 19(7), e0307138. https://doi.org/10.1371/journal.pone.0307138</mixed-citation>
      </ref>
      <ref id="R18">
        <mixed-citation>[18] Zhou, B., Jiang, D., Dai, Y., &amp; Hayat, T. (2023). Threshold dynamics and probability density function of a stochastic avian influenza epidemic model with nonlinear incidence rate and psychological effect. Journal of Nonlinear Science, 33(2), 29. https://doi.org/10.1007/s00332-022-09885-8</mixed-citation>
      </ref>
      <ref id="R19">
        <mixed-citation>[19] Shen, S., Ito, T., &amp; Hirose, T. (2025). Spatio-temporal gradient flow for efficient motion estimation in sparse point clouds. Journal of Robotics and Mechatronics, 37(6), 1327-1342. https://doi.org/10.20965/jrm.2025.p1327</mixed-citation>
      </ref>
      <ref id="R20">
        <mixed-citation>[20] Wang, H., Hu, Q., Li, B., Geng, Y., Su, C., Zhu, S., ... &amp; Chen, W. (2026). RTS-SLAM: A Trajectory Consistency-Driven Multi-Constraint Dynamic Feature-Rejection Method for Visual SLAM in Dynamic Environments. Sensors, 26(9), 2846. https://doi.org/10.3390/s26092846</mixed-citation>
      </ref>
      <ref id="R21">
        <mixed-citation>[21] Morris, J., Wang, Y., Kliniewski, M., &amp; Ila, V. (2025). Dynosam: Open-source smoothing and mapping framework for dynamic slam. IEEE Transactions on Robotics. https://doi.org/10.1109/tro.2025.3641813</mixed-citation>
      </ref>
      <ref id="R22">
        <mixed-citation>[22] Li, Z., Jiang, C., Sun, Y., Su, H., &amp; He, L. (2025). Precise feature removal method based on semantic and geometric dual masks in dynamic SLAM. Applied Sciences, 15(13), 7095. https://doi.org/10.3390/app15137095</mixed-citation>
      </ref>
      <ref id="R23">
        <mixed-citation>[23] Xu, W., Liu, S., Chen, R., Du, S., &amp; Wang, Y. (2026). Robust Multi-Modal Factor Graph Optimization for Distributed Collaborative LiDAR–Visual–Inertial SLAM. Applied Sciences, 16(10), 4677. https://doi.org/10.3390/app16104677</mixed-citation>
      </ref>
      <ref id="R24">
        <mixed-citation>[24] Chen, J., Jia, K., &amp; Wei, Z. (2025). Small but Mighty: A Lightweight Feature Enhancement Strategy for LiDAR Odometry in Challenging Environments. Remote Sensing, 17(15), 2656. https://doi.org/10.3390/rs17152656</mixed-citation>
      </ref>
      <ref id="R25">
        <mixed-citation>[25] Gopalakrishnan, V., Dey, N., &amp; Golland, P. (2026). PolyPose: Deformable 2D/3D Registration via Polyrigid Transformations. Advances in Neural Information Processing Systems, 38, 60633-60659. https://doi.org/10.48550/arXiv.2505.19256</mixed-citation>
      </ref>
      <ref id="R26">
        <mixed-citation>[26] Zheng, J., Li, Y., Zhao, J., Chen, Y., Xu, J., &amp; Xu, S. (2026). Residual Reweighting with Scan Normalization for LiDAR-Inertial Odometry in Degenerate Environments. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2026.3714871</mixed-citation>
      </ref>
      <ref id="R27">
        <mixed-citation>[27] Deng, Z., Li, C., Lin, W., &amp; Sun, L. (2026, June). GMM-LIO: Adaptive and Robust LiDAR-Inertial Odometry with Gaussian Mixture Model Voxel Map. In 2026 IEEE International Conference on Robotics and Automation (ICRA) (pp. 21276-21283). IEEE. https://doi.org/10.1109/icra57385.2026.11697299</mixed-citation>
      </ref>
      <ref id="R28">
        <mixed-citation>[28] Liao, Y., Xie, J., &amp; Geiger, A. (2022). Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3292-3310. https://doi.org/10.1109/tpami.2022.3179507</mixed-citation>
      </ref>
    </ref-list>
  </back>
</article>