Laser & Optoelectronics Progress, Volume. 61, Issue 4, 0437003(2024)

Visual SLAM Algorithm Based on Weighted Static in Dynamic Environment

Yong Li1,2, Haibo Wu1,2、*, Wan Li1,2, and Dongze Li1,2
Author Affiliations
  • 1Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
  • 2Key Laboratory of Intelligent Manufacturing Technology for Advanced Equipment in Yunnan Province, Kunming 650500, Yunnan, China
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    Figures & Tables(12)
    System framework diagram
    Improved YOLOv5s-seg network structure
    Two Ghost modules. (a) DFC attention; (b) GhostNetV2Bottleneck module; (c) GhostV2C3 module
    Schematic of the epipolar constraint. (a) Case of meeting the epipolar constraint; (b) case where the epipolar constraint is not met
    Removing dynamic feature points. (a) Semantic segmentation; (b) semantic segmentation removal; (c) pole constraint removal; (d) weighted geometric constraint removal
    Absolute trajectory error map, 3D trajectory error heat map, and relative pose error map. (a) fr3/walking_xyz sequence; (b) fr3/walking_rpt sequence; (c) fr3/sitting_static sequence
    Experiments using RGB-D cameras in a real dynamic scene. (a) Experiment scene; (b) experimental result
    Comparison of different algorithms in a real dynamic scene
    • Table 1. Comparison of the absolute trajectory error

      View table

      Table 1. Comparison of the absolute trajectory error

      SequenceORB-SLAM2Proposed algorithmImprovement percentage /%
      RMSEMedianS.D.RMSEMedianS.D.RMSEMedianS.D.
      w_half0.61730.50740.24950.02410.01840.011296.1096.3795.51
      w_rpy0.81540.65420.42360.02750.02040.015896.6396.8896.27
      w_static0.35180.26390.16070.00710.00540.003097.9897.9598.13
      w_xyz0.73230.66730.25640.01330.01130.006898.1898.3197.34
      s_staic0.00850.00740.00420.00640.0570.003324.7122.9721.42
    • Table 2. Comparison of the relative pose error

      View table

      Table 2. Comparison of the relative pose error

      SequenceORB-SLAM2Proposed algorithmImprovement percentage /%
      RMSEMedianS.D.RMSEMedianS.D.RMSEMedianS.D.
      w_half0.44580.23340.28590.02510.01950.012294.3791.6595.73
      w_rpy0.48030.1210.34160.03810.02740.026292.0677.3692.33
      w_static0.22140.0170.14630.00920.00920.004895.8495.0696.70
      w_xyz0.45120.16530.29230.01790.01390.009096.0391.5996.23
      s_staic0.00830.00720.00430.00760.00580.00388.4319.4411.63
    • Table 3. Comparison of the absolute trajectory error of some advanced SLAM algorithms

      View table

      Table 3. Comparison of the absolute trajectory error of some advanced SLAM algorithms

      SequenceDyna-SLAMDS-SLAMRDS-SLAMProposed algorithm
      RMSES.D.RMSES.D.RMSES.D.RMSES.D.
      w_half0.02960.01570.03030.01590.08070.04540.02410.0112
      w_rpy0.04150.02710.44420.23500.16040.08730.02750.0158
      w_static0.00680.00340.00810.00360.02060.0120.00710.0030
      w_xyz0.01570.00830.02470.01610.05710.02290.01330.0068
      s_staic0.00670.00460.00650.00330.00840.00430.00640.0033
    • Table 4. Time-consuming comparison of the tracking phase

      View table

      Table 4. Time-consuming comparison of the tracking phase

      MethodSemantic segmentation/detection time /msTime of other modules related to tracking /msTrack each frame time /msGPU
      ORB-SLAM238.23GeForceRTX1650
      Dyna-SLAM198.63Geometry module:239.52672.38GeForceRTX1650
      RDS-SLAM200Mask generation:5.3185.43GeForceRTX1650
      Proposed algorithm20.48Joint optimization:132.4490.56GeForceRTX1650
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    Yong Li, Haibo Wu, Wan Li, Dongze Li. Visual SLAM Algorithm Based on Weighted Static in Dynamic Environment[J]. Laser & Optoelectronics Progress, 2024, 61(4): 0437003

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    Paper Information

    Category: Digital Image Processing

    Received: May. 8, 2023

    Accepted: Jul. 24, 2023

    Published Online: Feb. 26, 2024

    The Author Email: Haibo Wu (whb_kust@kust.edu.cn)

    DOI:10.3788/LOP231254

    CSTR:32186.14.LOP231254

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