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  • Open Access

    ARTICLE

    PPS-SLAM: Dynamic Visual SLAM with a Precise Pruning Strategy

    Jiansheng Peng1,2,3,4,*, Wei Qian1, Hongyu Zhang1

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2849-2868, 2025, DOI:10.32604/cmc.2024.058028 - 17 February 2025

    Abstract Dynamic visual SLAM (Simultaneous Localization and Mapping) is an important research area, but existing methods struggle to balance real-time performance and accuracy in removing dynamic feature points, especially when semantic information is missing. This paper presents a novel dynamic SLAM system that uses optical flow tracking and epipolar geometry to identify dynamic feature points and applies a regional dynamic probability method to improve removal accuracy. We developed two innovative algorithms for precise pruning of dynamic regions: first, using optical flow and epipolar geometry to identify and prune dynamic areas while preserving static regions on stationary… More >

  • Open Access

    REVIEW

    Dynamic SLAM Visual Odometry Based on Instance Segmentation: A Comprehensive Review

    Jiansheng Peng1,2,*, Qing Yang1, Dunhua Chen1, Chengjun Yang2, Yong Xu2, Yong Qin2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 167-196, 2024, DOI:10.32604/cmc.2023.041900 - 30 January 2024

    Abstract Dynamic Simultaneous Localization and Mapping (SLAM) in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving. However, in the face of complex real-world environments, current dynamic SLAM systems struggle to achieve precise localization and map construction. With the advancement of deep learning, there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years, and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM. Compared to dynamic SLAM systems based on deep learning methods such… More >

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