Chinese Journal of Liquid Crystals and Displays, Volume. 40, Issue 5, 727(2025)

Visual SLAM algorithm based on dynamic feature elimination and dense mapping

Heng ZHANG, Lei WANG*, Pengchang ZHANG, Jian CHANG, and Xing HE
Author Affiliations
  • School of Mechanical Engineering,Shaanxi University of Technology,Hanzhong 723001,China
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    Figures & Tables(17)
    System framework
    Improved YOLOV8n structure
    FasterNet network architecture
    SimAM network architecture
    Epipolar constraints
    Flow chart of visual word-bag model
    Comparison of feature point removal effects
    Comparative results of ATE between two systems on TUM dataset
    Comparison of dense point cloud mapping
    • Table 0. [in Chinese]

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      Table 0. [in Chinese]

      算法1 动态特征点过滤算法

      输入:阈值T;先前帧F1;当前帧F2

      输出:静态特征点合集S

      1.P1=Extractor_Keypoints(F1)//先前帧特征点提取

      2.P2=Extractor_Keypoints(F2)//当前帧特征点提取

      3.K=Calculate_Optical_FlowLK(F1F2P2)//计算两帧的稀疏光流

      4.F=Estimate_Fundamental_Matrix(P1P2)//估计基础矩阵

      5. for i in range(len(P2))

      if not Semantic_Prior(P2i])then//特征点坐标不与动态物体检测框重合

      append P2i]to S

      else if Semantic_Prior(P2i])then

      if Dynamic_Value(P2i]<T)and judge(P2i],K)is ture then//对极几何约束与光流法先后判定

      append P2i]to S

      end if

    • Table 1. Ablation experiment

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      Table 1. Ablation experiment

      实验序号FasterNetSimAM参数量GFLOPs精确度/%mAP@0.5/%
      Baseline315 7188.972.375.2
      1193 4416.269.473.4
      2318 4318.973.476.3
      3194 5316.371.474.8
    • Table 2. Performance comparison of different BOW

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      Table 2. Performance comparison of different BOW

      词袋空间大小/MB平均加载时间/ms
      原有词袋145.38 213.5
      二进制词袋44.4772.4
    • Table 3. Comparison of ATE experimental results

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      Table 3. Comparison of ATE experimental results

      序列ORB-SLAM3本文算法提升率/%
      RMSEMeanMedianS.D.RMSEMeanMedianS.D.RMSEMeanMedianS.D.
      Walking_xyz0.534 20.487 20.458 60.218 00.015 10.013 30.011 90.007 197.1797.2797.4094.54
      Walking_rpy1.133 40.956 61.098 50.607 90.024 80.031 70.023 60.011 796.0497.8197.8598.07
      Walking_half0.360 30.284 10.243 00.221 60.028 30.025 10.023 10.013 192.1491.1690.4994.08
      Sitting_static0.008 90.007 80.007 00.004 20.006 10.006 20.005 50.003 331.4620.5121.4221.42
    • Table 4. Comparison of experimental results of RPE translation part

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      Table 4. Comparison of experimental results of RPE translation part

      序列ORB-SLAM3本文算法提升率/%
      RMSEMeanMedianS.D.RMSEMeanMedianS.D.RMSEMeanMedianS.D.
      Walking_xyz0.816 50.637 00.514 20.510 70.022 10.019 60.018 00.010 297.2996.9296.4998.01
      Walking_rpy1.770 21.403 01.077 91.079 50.063 10.047 60.037 30.041 396.4396.6096.5396.17
      Walking_half0.506 50.367 30.358 70.348 80.040 00.035 90.034 40.017 692.1090.2290.4094.95
      Sitting_static0.014 00.012 10.010 40.007 00.010 00.008 90.008 20.004 628.5726.4421.1534.28
    • Table 5. Comparison of experimental results of RPE rotation part

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      Table 5. Comparison of experimental results of RPE rotation part

      序列ORB-SLAM3本文算法提升率/%
      RMSEMeanMedianS.D.RMSEMeanMedianS.D.RMSEMeanMedianS.D.
      Walking_xyz16.07912.4759.868 910.1440.612 70.487 10.417 80.371 696.1896.0995.7696.33
      Walking_rpy36.60728.98721.16022.3561.298 80.971 80.749 50.861 896.4596.6496.4596.14
      Walking_half10.3147.553 97.438 37.023 40.895 90.800 80.745 10.401 491.3189.3989.9894.28
      Sitting_static0.375 20.338 40.323 80.162 10.339 90.307 70.291 90.144 49.409.079.8510.91
    • Table 6. Comparison of the error results between proposed algorithm and the same type of algorithms

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      Table 6. Comparison of the error results between proposed algorithm and the same type of algorithms

      SequenceDS-SLAMDynaSLAMSG-SLAMRDS-SLAMOurs
      RMSES.D.RMSES.D.RMSES.D.RMSES.D.RMSES.D.
      Walking_xyz0.027 40.015 80.019-0.016 30.007 30.057 30.023 10.015 10.007 1
      Walking_rpy0.435 70.241 10.037-0.034 50.018 40.165 40.086 30.024 80.011 7
      Walking_half0.030 30.014 90.024-0.027 10.014 50.081 10.045 80.028 30.013 1
      Sitting_static0.006 50.003 30.006-0.006 80.005 90.008 40.004 30.006 10.003 3
    • Table 7. Real-time comparison of proposed algorithm with similar algorithms

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      Table 7. Real-time comparison of proposed algorithm with similar algorithms

      SLAM系统平均每帧处理时间/ms
      ORB-SLAM331.42
      RDS-SLAM59.35
      DS-SLAM61.47
      SG-SLAM40.82
      DynaSLAM698.34
      Ours49.73
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    Heng ZHANG, Lei WANG, Pengchang ZHANG, Jian CHANG, Xing HE. Visual SLAM algorithm based on dynamic feature elimination and dense mapping[J]. Chinese Journal of Liquid Crystals and Displays, 2025, 40(5): 727

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    Paper Information

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    Received: Nov. 13, 2024

    Accepted: --

    Published Online: Jun. 18, 2025

    The Author Email: Lei WANG (leiwang@xaut.edu.cn)

    DOI:10.37188/CJLCD.2024-0325

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