• Laser & Optoelectronics Progress
  • Vol. 57, Issue 8, 081024 (2020)
Shidong Lu1, Meiyi Tu1、*, Xiaoyong Luo2, and Chao Guo2
Author Affiliations
  • 1Hubei Provincial Research Institute of Land and Resources, Wuhan, Hubei 430071, China
  • 2Hunan Glonavin Information Technology Co., Ltd., Changsha, Hunan 410006, China
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    DOI: 10.3788/LOP57.081024 Cite this Article Set citation alerts
    Shidong Lu, Meiyi Tu, Xiaoyong Luo, Chao Guo. Laser SLAM Pose Optimization Algorithm Based on Graph Optimization Theory and GNSS[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081024 Copy Citation Text show less
    Algorithm framework diagram
    Fig. 1. Algorithm framework diagram
    Pose graph integrated with GNSS position data
    Fig. 2. Pose graph integrated with GNSS position data
    Accuracy of point cloud registration pose estimation
    Fig. 3. Accuracy of point cloud registration pose estimation
    GNSS-SLAM pose correction when there is no loopback
    Fig. 4. GNSS-SLAM pose correction when there is no loopback
    Diagram of loop closure optimization
    Fig. 5. Diagram of loop closure optimization
    Loop closure detection diagram
    Fig. 6. Loop closure detection diagram
    Hardware picture of experiment
    Fig. 7. Hardware picture of experiment
    Test without loopback in urban environment. (a) With GNSS pose optimization; (b) without GNSS pose optimization
    Fig. 8. Test without loopback in urban environment. (a) With GNSS pose optimization; (b) without GNSS pose optimization
    Loop trajectory test in urban environment. (a) With GNSS pose optimization; (b) without GNSS pose optimization
    Fig. 9. Loop trajectory test in urban environment. (a) With GNSS pose optimization; (b) without GNSS pose optimization
    Test without loopback in non-urban environment. (a) With GNSS pose optimization; (b) without GNSS pose optimization
    Fig. 10. Test without loopback in non-urban environment. (a) With GNSS pose optimization; (b) without GNSS pose optimization
    Picture of non-urban environment
    Fig. 11. Picture of non-urban environment
    Loop trajectory test in non-urban environment. (a) With GNSS pose optimization; (b) without GNSS pose optimization
    Fig. 12. Loop trajectory test in non-urban environment. (a) With GNSS pose optimization; (b) without GNSS pose optimization
    HardwareSoftware
    SensorModels (parameters)NameVersion
    LiDARRobosense RS-LiDAR-16(ranging accuracy ±0.02 m,distance measuring range of 100-150 m)Operating SystemUbuntu16.04 LTS
    GPS moduleGlonavin GNSS Single site location module(Horizontal position precision 1 m,vertical position precision 2 m)ROS(robot operating system)Kinetic
    Hardware platformCPU (Intel i7 8core 1.8 GHZ),RAM(16 GB)Point cloud registrationalgorithmICP
    Table 1. Experimental software and hardware related instructions and parameters
    ExperimentWith GNSS poseoptimizationWithout GNSSpose optimization
    δdδyδzδdδyδz
    WithoutloopbackUrban1.110.070.973.830.353.56
    Non-urban0.620.170.0925.2514.303.32
    Loop trajectoryUrbanFirst loop drift0.200.120.1530.218.9815.38
    Second loop drift0.110.100.0851.9510.3039.76
    Non-urbanFirst loop drift0.160.140.055.524.920.33
    Second loop drift0.100.090.0477.4919.3055.75
    Table 2. Trajectory drift comparisonm
    Shidong Lu, Meiyi Tu, Xiaoyong Luo, Chao Guo. Laser SLAM Pose Optimization Algorithm Based on Graph Optimization Theory and GNSS[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081024
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