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Edge Assisted Fast Optical Flow Matching SLAM in Underground Rescue Environments

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Pattern Recognition and Computer Vision (PRCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15040))

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Abstract

Simultaneous localization and mapping (SLAM) technology provides basic location services and environment sensing for underground coal mine rescue. Due to the low illumination and less texture underground environment, as well as the restricted computing resources, existing SLAM systems are prevented from working stably in real-time on mobile devices. In this paper, we propose a fast optical flow matching SLAM (FOFM-SLAM) based on edge computing in underground rescue environments. This is the first edge-assisted SLAM system that adopts a two-stage sparse optical flow tracking method based on image pyramid for feature matching and refines the keypoint correspondences by outlier filtering strategy. Further, we design a keyframe selection strategy based on limited viewpoint transfer, taking into account factors such as parallax and tracking points to ensure tracking stability. We perform comprehensive experiments on TUM and ETH3D RGB-D datasets and fully implement FOFM-SLAM on various types of devices. Results reveal that FOFM-SLAM achieves a relative pose error of 0.51cm, and the tracking time is improved by about 40% on the ETH3D dataset compared with the existing solutions. Finally, we implement the field tests in the coal mine to present the practicality.

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Acknowledgement

This work was supported by the Natural Science Foundation of Shenzhen City under Grant No. JCYJ20230807154300002.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China

    Chuanjie Fei, Qiliang Zhang, Zijie Cai, Yuhao Jin, Kangjia He & Kuiyuan Zhang

Authors
  1. Chuanjie Fei
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  2. Qiliang Zhang
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  3. Zijie Cai
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  4. Yuhao Jin
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  5. Kangjia He
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  6. Kuiyuan Zhang
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Corresponding author

Correspondence to Qiliang Zhang .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Zhouchen Lin

  2. Nankai University, Tianjin, China

    Ming-Ming Cheng

  3. Chinese Academy of Sciences, Beijing, China

    Ran He

  4. Xinjiang University, Ürümqi, Xinjiang, China

    Kurban Ubul

  5. Xinjiang University, Ürümqi, China

    Wushouer Silamu

  6. Peking University, Beijing, China

    Hongbin Zha

  7. Tsinghua University, Beijing, China

    Jie Zhou

  8. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

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Fei, C., Zhang, Q., Cai, Z., Jin, Y., He, K., Zhang, K. (2025). Edge Assisted Fast Optical Flow Matching SLAM in Underground Rescue Environments. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15040. Springer, Singapore. https://doi.org/10.1007/978-981-97-8792-0_1

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  • DOI: https://doi.org/10.1007/978-981-97-8792-0_1

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