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Laser & Optoelectronics Progress, Volume. 57, Issue 20, 201012(2020)

Semantic Mapping Based on YOLOv3 and Visual SLAM

Bin Zou1,2, Siyang Lin1、*, and Zhishuai Yin1,2
Author Affiliations
  • 1Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, Hubei 430070, China
  • 2Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan, Hubei 430070, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
    Equations (0)
    References (19)
    Cited By (10)
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    Figures & Tables(10)
    Flow chart of YOLOv3 algorithm

    Fig. 1. Flow chart of YOLOv3 algorithm

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    Structure of YOLOv3

    Fig. 2. Structure of YOLOv3

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    Supervoxel clustering

    Fig. 3. Supervoxel clustering

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    Graph segmentation of surface patches

    Fig. 4. Graph segmentation of surface patches

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    Object information label. (a) Original point cloud; (b) object detection; (c) semantic label

    Fig. 5. Object information label. (a) Original point cloud; (b) object detection; (c) semantic label

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    Optimization of ORB characteristic point extraction. (a) Original ORB characteristic point extraction; (b) optical flow tracking ORB characteristic point

    Fig. 6. Optimization of ORB characteristic point extraction. (a) Original ORB characteristic point extraction; (b) optical flow tracking ORB characteristic point

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    Comparison of point cloud segmentation results. (a) Algorithm 1; (b) algorithm 2; (c) proposed algorithm

    Fig. 7. Comparison of point cloud segmentation results. (a) Algorithm 1; (b) algorithm 2; (c) proposed algorithm

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    Verifying semantic map. (a) System operating in real-time; (b) original map; (c) semantic map

    Fig. 8. Verifying semantic map. (a) System operating in real-time; (b) original map; (c) semantic map

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    • Table 1. Running time of different algorithms

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      Table 1. Running time of different algorithms

      AlgorithmRunning time /ms
      Algorithm 118073
      Algorithm 29825
      Proposed algorithm10531

    • Table 2. Comparison of experimental results of map construction

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      Table 2. Comparison of experimental results of map construction

      Algorithmfreiburg3_sittingfreiburg3_long_office
      Number of pointsMap size /MbitTime /sNumber of pointsMap size /MbitTime /s
      Proposed algorithm210654573.50.08715467152151.30.1318
      ORB with YOLOv3450307191.40.129598408731900.1841
      ORB with Mask R-CNN[19]438223286.30.17349165934177.80.2506
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    Bin Zou, Siyang Lin, Zhishuai Yin. Semantic Mapping Based on YOLOv3 and Visual SLAM[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201012

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    Paper Information

    Category: Image Processing

    Received: Dec. 24, 2019

    Accepted: Feb. 25, 2020

    Published Online: Oct. 13, 2020

    The Author Email: Siyang Lin (xyz5016@whut.edu.cn)

    DOI:10.3788/LOP57.201012

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology