Volume 22 Issue 3
Jun.  2022
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2022  >  22(3): 251-262
GAO Yang, CAO Wang-xin, XIA Hong-yao, ZHAO Yi-hui. Driverless vehicle positioning algorithm based on simultaneous positioning and mapping in low-visibility environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 251-262. doi: 10.19818/j.cnki.1671-1637.2022.03.020
Citation: GAO Yang, CAO Wang-xin, XIA Hong-yao, ZHAO Yi-hui. Driverless vehicle positioning algorithm based on simultaneous positioning and mapping in low-visibility environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 251-262. doi: 10.19818/j.cnki.1671-1637.2022.03.020
shu

Driverless vehicle positioning algorithm based on simultaneous positioning and mapping in low-visibility environment

doi: 10.19818/j.cnki.1671-1637.2022.03.020
  • 1.

    School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    Xi'an Coal Mining Machinery Co., Ltd., Xi'an 710200, Shaanxi, China

Funds:

National Key Research and Development Program of China 2019YFB1600100

Natural Science Foundation of Shaanxi Province 2019JLP-07

More Information
  • Author Bio:

    GAO Yang(1982-), male, associate professor, PhD, nchygy@chd.edu.cn

  • Received Date: 2021-12-11
  • Publish Date: 2022-06-25
    Abstract
  • In order to achieve high-precision positioning for driverless vehicles in a large-scale and low-light environment, a fused positioning algorithm LVG_SLAM was proposed based on the system framework of the VINS-Mono algorithm. In LVG_SLAM, a RFAST low-light image enhancement module and a VG fusion positioning module were proposed and then added. The RFAST low-light image enhancement module applied a wavelet transform to separate the detailed information from the brightness information. In the RFAST module, the unified threshold and mean filter were applied to filter the detailed noisy information from the oringinal image while the bilateral texture filter algorithm was applied to enhance the detail information. After that, the multi-scale retinex algorithm was proposed to further enhance the contrast of the image to improve the success rate of corner extraction in a low-light environment, benefit from which, both the stability of image tracking and the robustness of the positioning algorithm were improved. Using an unscented Kalman filter (UKF) algorithm, the VG fusion positioning module loosely fused the positioning information from both the global navigation satellite system (GNSS) and the inertial navigation equipment. The fused positioning result was introduced as a constraint into the back end of the LVG_SLAM algorithm, benefit from which, the influence of cumulative error on the positioning accuracy of the algorithm was reduced by a joint nonlinear optimization. Analysis results show that compared with the VINS-Mono algorithm, the LVG_SLAM algorithm performs better on the EuRoC and Kitti public datasets, and the root mean square error reduces by 38.76% and 58.39%, respectively, so that the motion trajectory estimated by the LVG_SLAM algorithm is closer to the real trajectory. In an experiment of night road scene, the LVG_SLAM algorithm successfully constrains the positioning error into a certain range, and detects the closed loop, which greatly improves the positioning performance. The root mean square error, average error, maximum error, and median error reduce by 79.61%, 82.50%, 71.31%, and 83.77%, respectively. Compared with the VINS-Mono algorithm, the proposed LVG_SLAM algorithm has obvious advantages in both positioning accuracy and robustness. 4 tabs, 12 figs, 26 refs.

     

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    • References(26)
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