Optics and Precision Engineering, Volume. 32, Issue 5, 752(2024)

Binocular vision SLAM with fused point and line features in weak texture environment

Kun GONG1... Xin XU1, Xiaoqing CHEN2, Yuelei XU1,* and Zhaoxiang ZHANG1 |Show fewer author(s)
Author Affiliations
  • 1Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an70072, China
  • 2National Innovation Institute of Defense Technology, Academy of Military Science, Beijing100850, China
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    Addressing the challenge of trajectory drift in visual Simultaneous Localization and Mapping (SLAM) due to point features in texture-deficient indoor settings, this study introduces a binocular visual SLAM system that combines point and line features. It emphasizes the extraction and matching of line features within binocular visual SLAM. An enhanced line feature extraction technique, based on the Line Segment Detector (LSD) algorithm, is proposed. This includes improvements like length and gradient filtering, and the amalgamation of short lines. Additionally, the matching issue is redefined as an optimization challenge, creating a cost function based on geometric constraints. A novel, efficient line segment triangulation approach, leveraging the L1-norm sparse solution, is developed for effective line matching and triangulation. Experimental evidence shows that our method surpasses traditional descriptor-based approaches across various datasets, especially in texture-sparse indoor areas, achieving a remarkable average matching accuracy of 91.67% and a swift average matching time of 7.4 ms. Employing this technique, our binocular visual SLAM system records positioning errors of 1.24, 7.49, and 3.67 m on texture-sparse datasets, outperforming leading algorithms like ORBSLAM2 and PL-SLAM in positioning precision.

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    Kun GONG, Xin XU, Xiaoqing CHEN, Yuelei XU, Zhaoxiang ZHANG. Binocular vision SLAM with fused point and line features in weak texture environment[J]. Optics and Precision Engineering, 2024, 32(5): 752

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    Paper Information

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    Received: Apr. 21, 2023

    Accepted: --

    Published Online: Apr. 2, 2024

    The Author Email: XU Yuelei (xuyuelei@nwpu.edu.cn)

    DOI:10.37188/OPE.20243205.0752

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