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A Visual Inertial SLAM Method for Fusing Point and Line Features

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Advances in Neural Networks – ISNN 2024 (ISNN 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14827))

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Abstract

The current SLAM methods generally have some drawbacks: such as poor stability and long time-consuming SLAM tasks; in order to solve the problem of poor positioning accuracy of SLAM tasks due to the drawbacks of these SLAM methods, the quality and speed of line feature extraction are improved by improving the traditional line feature extraction method LSD, and the point-line feature fusion with IMU information is fused into the visual inertial SLAM system, which can overcome the difficulties of some previous SLAM systems in facing special environments for SLAM tasks. The experimental validation of this paper’s method is carried out by using data from the publicly available dataset EuRoC, and the experimental results show that this paper’s visual inertial SLAM method of fusing point and line features has a high positioning accuracy.

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Acknowledgments

Project supported by the National Natural Science Foundation of China (Grant Nos. U20A20227, 62076208, 62076207), Chongqing Talent Plan “Contract System” Project (Grant No. CQYC20210302257), National Key Laboratory of Smart Vehicle Safety Technology Open Fund Project (Grant No. IVSTSKL-202309) and funded by the Chongqing Technology Innovation and Application Development Special Major Project (Grant No. CSTB2023TIAD-STX0020).

Author information

Authors and Affiliations

  1. College of Artificial Intelligence, Southwest University, Chongqing, 400715, China

    Yunfei **ao, Huajun Ma, Shukai Duan & Lidan Wang

  2. National & Local Joint Engineering Research Center of Intelligent Transmission and Control Technology, Chongqing, 400715, China

    Lidan Wang

  3. Chongqing Key Laboratory of Brain-Inspired Computing and Intelligent Chips, Chongqing, 400715, China

    Lidan Wang

  4. Key Laboratory of Luminescence Analysis and Molecular Sensing, Ministry of Education, Chongqing, 400715, China

    Lidan Wang

Authors
  1. Yunfei **ao
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  2. Huajun Ma
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  3. Shukai Duan
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  4. Lidan Wang
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Corresponding author

Correspondence to Lidan Wang .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    **nyi Le

  2. South China University of Technology, Guangzhou, China

    Zhijun Zhang

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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**ao, Y., Ma, H., Duan, S., Wang, L. (2024). A Visual Inertial SLAM Method for Fusing Point and Line Features. In: Le, X., Zhang, Z. (eds) Advances in Neural Networks – ISNN 2024. ISNN 2024. Lecture Notes in Computer Science, vol 14827. Springer, Singapore. https://doi.org/10.1007/978-981-97-4399-5_25

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  • DOI: https://doi.org/10.1007/978-981-97-4399-5_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-4398-8

  • Online ISBN: 978-981-97-4399-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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