• Laser & Optoelectronics Progress
  • Vol. 59, Issue 10, 1015002 (2022)
Lin Li1、*, Huaiyu Wu1、2, and Tianyu Zhang2
Author Affiliations
  • 1School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei , China
  • 2Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081, Hubei , China
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    DOI: 10.3788/LOP202259.1015002 Cite this Article Set citation alerts
    Lin Li, Huaiyu Wu, Tianyu Zhang. Constructing Semantic Map of Mobile Robots Based on Improved DeepLab V3+[J]. Laser & Optoelectronics Progress, 2022, 59(10): 1015002 Copy Citation Text show less
    Proposed framework
    Fig. 1. Proposed framework
    ASPP module in DeepLab V3+
    Fig. 2. ASPP module in DeepLab V3+
    Standard convolution decomposition
    Fig. 3. Standard convolution decomposition
    Schematic of corner extraction
    Fig. 4. Schematic of corner extraction
    Violence matching result
    Fig. 5. Violence matching result
    Feature matching results after screening
    Fig. 6. Feature matching results after screening
    Algorithm training overall loss curve
    Fig. 7. Algorithm training overall loss curve
    Comparison of segmentation results between DeepLab V3+ and improved DeepLab V3+. (a) Input images; (b) ground truth; (c) segmentation results of DeepLab V3+; (d) segmentation results of improved DeepLab V3+
    Fig. 8. Comparison of segmentation results between DeepLab V3+ and improved DeepLab V3+. (a) Input images; (b) ground truth; (c) segmentation results of DeepLab V3+; (d) segmentation results of improved DeepLab V3+
    3D point cloud maps. (a) Perspective 1; (b) perspective 2
    Fig. 9. 3D point cloud maps. (a) Perspective 1; (b) perspective 2
    3D point cloud after segmentation. (a) Perspective 1; (b) perspective 2
    Fig. 10. 3D point cloud after segmentation. (a) Perspective 1; (b) perspective 2
    Semantic maps. (a) Perspective 1; (b) perspective 2
    Fig. 11. Semantic maps. (a) Perspective 1; (b) perspective 2
    Semantic segmentation results of improved DeepLab V3+. (a) Scene Ⅰ; (b) scene Ⅱ; (c) semantic segmentation result under scene I; (d) semantic segmentation result under scene Ⅱ
    Fig. 12. Semantic segmentation results of improved DeepLab V3+. (a) Scene Ⅰ; (b) scene Ⅱ; (c) semantic segmentation result under scene I; (d) semantic segmentation result under scene Ⅱ
    Semantic maps from different perspectives. (a) 3D map; (b) semantic map from perspective 1; (c) semantic map from perspective 2; (d) semantic map from perspective 3
    Fig. 13. Semantic maps from different perspectives. (a) 3D map; (b) semantic map from perspective 1; (c) semantic map from perspective 2; (d) semantic map from perspective 3
    Training parameterValue
    Batch size4
    Learning rate0.0001
    Power0.9
    Epoch100
    Table 1. Network parameter selection
    AlgorithmmIOU /%PA /%Number of parametersTime /ms
    DeepLab V3+78.2789.439.03×107318
    Improved DeepLab V3+76.9587.183.53×10675
    Table 2. Algorithm evaluation index comparison
    DatasetNumber of framesRMSE of relative trajectory
    RGBD‑SLAMORB‑SLAMProposed algorithm
    fr1/floor12420.00440.00410.0040
    fr1/xyz7980.00580.00590.0057
    fr2/36014310.0350.0330.0276
    fr2/desk29650.00370.00360.0035
    Table 3. RMSE of relative trajectory
    DatasetICP algorithmImproved ICP algorithm
    Average time /sAverage number of iterationsAverage time /sAverage number of iterations
    fr1/floor0.04290.0163
    fr2/desk0.03370.0122
    Table 4. Speed of motion estimation and number of iterations
    MethodNumber of point cloudsTotal map construction time /msMap size /Mbit
    ORB +YOLOv34132273508145.7
    ORB+MASK-RCNN3740152461128.2
    ORB+DeepLab V3+3309853439110.5
    Proposed method117659223171.2
    Table 5. Comparison of map construction performance of different methods
    ScenemIOU /%Processing time /ms
    76.5986
    77.9253
    Table 6. Segmentation performance under different scenes
    Lin Li, Huaiyu Wu, Tianyu Zhang. Constructing Semantic Map of Mobile Robots Based on Improved DeepLab V3+[J]. Laser & Optoelectronics Progress, 2022, 59(10): 1015002
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