Volume 10 Issue 4
Aug.  2021
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Article Navigation >  Journal of Radars  > 2021  >  10(4): 622-631
WANG Junjie, FENG Dejun, WANG Zhisong, et al. Synthetic aperture rader imaging characteristics of electronically controlled time-varying electromagnetic materials[J]. Journal of Radars, 2021, 10(6): 865–873. doi: 10.12000/JR21104
Citation: DANG Xiangwei, QIN Fei, BU Xiangxi, et al. A robust perception algorithm based on a radar and LiDAR for intelligent driving[J]. Journal of Radars, 2021, 10(4): 622–631. doi: 10.12000/JR21036
shu

A Robust Perception Algorithm Based on a Radar and LiDAR for Intelligent Driving

DOI: 10.12000/JR21036
  • 1.

    National Key Laboratory of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049, China

Funds:  The National Ministries Foundation
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  • Corresponding author: LIANG Xingdong, xdliang@mail.ie.ac.cn
  • Received Date: 2021-03-19
  • Rev Recd Date: 2021-04-28
    • Available Online: 2021-05-20
  • Publish Date: 2021-08-28
    Abstract
  • Multi-sensor fusion perception is one of the key technologies to realize intelligent automobile driving, and it has become a hot issue in the field of intelligent driving. However, because of the limited resolution of millimeter-wave radars, the interference of noise, clutter, and multipath, and the influence of weather on LiDAR, the existing fusion algorithm cannot easily achieve accurate fusion of the data of two sensors and obtain robust results. To address the problem of accurate and robust perception in intelligent driving, this study proposes a robust perception algorithm that combines millimeter-wave radar and LiDAR. Using a new method of spatial correction based on feature-based two-step registration, the precise spatial synchronization of the 3D LiDAR and 2D radar point clouds is realized. The improved millimeter-wave radar filtering algorithm is used to reduce the influence of noise and multipath on the radar point cloud. Then, according to the novel fusion method proposed in this study, the data of the two sensors are fused to obtain accurate and robust sensing results, which solves the problem of the influence of smoke on LiDAR performance. Finally, we conducted multiple sets of experiments in a real environment to verify the effectiveness and robustness of our method. Even in extreme environments such as smoke, we can still achieve accurate positioning and robust mapping. The environment map established by the fusion method proposed in this study is more accurate than that established by a single sensor. Moreover, the location error obtained can be reduced by at least 50%.

     

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    • References(20)
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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