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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2018  >  18(3): 228-238
KANG Jun-min, ZHAO Xiang-mo, YANG Di. Corner feature extraction of 2D lidar data[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 228-238. doi: 10.19818/j.cnki.1671-1637.2018.03.023
Citation: KANG Jun-min, ZHAO Xiang-mo, YANG Di. Corner feature extraction of 2D lidar data[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 228-238. doi: 10.19818/j.cnki.1671-1637.2018.03.023
shu

Corner feature extraction of 2D lidar data

doi: 10.19818/j.cnki.1671-1637.2018.03.023
  • 1.

    School of Economy and Finance, Xi'an International Studies University, Xi'an 710128, Shaanxi, China

  • 2.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 3.

    School of Business, Xi'an International Studies University, Xi'an 710128, Shaanxi, China

More Information
  • Author Bio:

    KANG Jun-min(1978-), male, lecturer, PhD, 9578577@qq.com

    ZHAO Xiang-mo(1966-), male, professor, PhD, xmzhao@chd.edu.cn

  • Received Date: 2018-01-05
  • Publish Date: 2018-06-25
    Abstract
  • In order to enhance the robustness of the corner feature recognition in the driving environment by the unmanned vehicle and improve the recognition speed of the corner feature, based on the relative difference between bivariate normal probability density map values of observation points, a corner feature extraction method was proposed. The observation data set was mapped to the bivariate normal probability density space, and the mapping value of each observation point was obtained. The mapping results were normalized, and the numerical differences caused by the covariances were eliminated. The positions of peaks and troughs were found in the mapped numerical curve. The observation point corresponding to the peak was closest to the mean point, and the observation point corresponding to the trough was closest to the inflection point. Whether the set of observed data meets the edge length requirement of the corner features was determined by using the relative heights of peaks and troughs. Thecoordinates of the original observation data points corresponding to the troughs were used as corner features to construct the environment feature map. Test result shows that the extraction method can process sparse observation data with more than 63 observation points and angular resolution of the observation point greater than 1°. Therefore, in large-scale outdoor environment and indoor environment, the extraction method can stably identify large corner points. When the observation data points are less than 180, the maximum processing time is less than 5 ms, and the average processing time is less than 1.9 ms, so the extraction method has good real-time performance, which is conducive for decreasing the time required for designing the environment feature map. The extraction method extracts the corner features according to the bivariate normal probability density of the observation data, is insensitive to the observation error and the scale and shape of the corner feature, and can effectively improve the robustness of corner feature recognition.14 figs, 25 refs.

     

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