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Acta Optica Sinica, Volume. 39, Issue 5, 0528003(2019)

Robust Localization Based on Kernel Density Estimation in Dynamic Diverse City Scenes Using Lidar

Rendong Wang*, Hua Li, Kai Zhao, and Youchun Xu**
Author Affiliations
  • Army Military Transportation University, Tianjin 300161, China
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    AbstractGet PDF(in Chinese)
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    Figures & Tables(9)
    High-accuracy maps constructed by proposed algorithm. (a) Height map; (b) reflectivity map

    Fig. 1. High-accuracy maps constructed by proposed algorithm. (a) Height map; (b) reflectivity map

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    Flow chart of proposed localization algorithm

    Fig. 2. Flow chart of proposed localization algorithm

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    Satellite map of experimental section

    Fig. 3. Satellite map of experimental section

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    Moving trajectories before and after localization by proposed algorithm

    Fig. 4. Moving trajectories before and after localization by proposed algorithm

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    Comparison of localization results in experiment 1 by three algorithms. (a) Lateral error; (b) longitudinal error; (c) heading angle error

    Fig. 5. Comparison of localization results in experiment 1 by three algorithms. (a) Lateral error; (b) longitudinal error; (c) heading angle error

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    Localization results of cross road. (a) Aerial view; (b) 3D view; (c) forward camera view; (d) HF algorithm; (e) ML-RANSAC algorithm; (f) proposed algorithm

    Fig. 6. Localization results of cross road. (a) Aerial view; (b) 3D view; (c) forward camera view; (d) HF algorithm; (e) ML-RANSAC algorithm; (f) proposed algorithm

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    Localization results of straight road. (a) Aerial view; (b) 3D view; (c) forward camera view; (d) HF algorithm; (e) ML-RANSAC algorithm; (f) proposed algorithm

    Fig. 7. Localization results of straight road. (a) Aerial view; (b) 3D view; (c) forward camera view; (d) HF algorithm; (e) ML-RANSAC algorithm; (f) proposed algorithm

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    Horizontal position error distributions by three algorithms under different initial pose deviations. (a) Scatter diagram; (b) histogram

    Fig. 8. Horizontal position error distributions by three algorithms under different initial pose deviations. (a) Scatter diagram; (b) histogram

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    • Table 1. Localization statistical results by three algorithms under different initial pose deviations

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      Table 1. Localization statistical results by three algorithms under different initial pose deviations

      Deviation /mAlgorithmHorizontal root-mean-square error /mLongitudinal root-mean-square error /mLateral root-mean-square error /mHeading angleroot-mean-square error /(°)Phoriz /%Time-cost /s
      Lateral error <0.1 mLateral error <0.4 mMeanStandard deviation
      HF0.76480.44130.56510.564736.2385.420.92480.8710
      2.5ML-RANSAC0.21890.15330.12050.219119.1792.930.46340.2934
      Proposed0.21770.19910.04760.141057.0095.310.34490.2394
      HF7.01424.50704.33662.076510.6451.774.912911.2041
      5.0ML-RANSAC1.99421.91820.31660.342720.6387.800.50830.3859
      Proposed0.14110.11980.05100.153954.9195.950.34580.1978
      HF15.8015.462214.41405.563209.0931.31259.6273
      10.0ML-RANSAC2.28802.21570.30600.378117.7680.440.63320.5340
      Proposed0.23200.20010.07490.216753.0593.180.59940.9812
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    Rendong Wang, Hua Li, Kai Zhao, Youchun Xu. Robust Localization Based on Kernel Density Estimation in Dynamic Diverse City Scenes Using Lidar[J]. Acta Optica Sinica, 2019, 39(5): 0528003

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Dec. 17, 2018

    Accepted: Jan. 23, 2019

    Published Online: May. 10, 2019

    The Author Email:

    DOI:10.3788/AOS201939.0528003

    Topics

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