Reliable positioning method of intelligent vehicles based on factor graph in GNSS-denied environment

被引：0
|
作者：
Hu Y. [1 ]
Li X. [1 ]
Xu Q. [1 ]
Yuan J. [2 ]
Kong X. [1 ]
机构：
[1] School of Instrument Science and Engineering, Southeast University, Nanjing
[2] Traffic Management Research Institute of the Ministry of Public Security, Wuxi
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2021年 / 42卷 / 11期
关键词：
Confidence interval; Error of non line of sight; Factor graph; GNSS-denied; Vehicle positioning;
D O I：
10.19650/j.cnki.cjsi.J2108296
中图分类号：
学科分类号：
摘要：
Aiming at the demand for accurate and reliable positioning of intelligent vehicles in GNSS-denied environments such as indoor parking lots and tunnels, a low-cost fusion positioning method based on factor graph is proposed. Firstly, a UWB/INS dynamic fusion positioning framework based on factor graph is established; secondly, through making full use of the static characteristics of the UWB base stations, the maximum theoretical distances between the base stations and the intelligent vehicle are calculated via the position and position confidence interval inferred from the factor graph, and compared with the distance measured by UWB to detect the non-line-of-sight signals; lastly, an adaptive fusion rule is formulated to guide the fusion of UWB/INS under different conditions, the final positioning result is obtained, and the reliable positioning of intelligent vehicles in GNSS-denied environments is achieved. The real vehicle tests show that the proposed fusion positioning method can achieve the positioning accuracy of 0.622 m, which is more than one time improvement compared with that of the traditional least square method. The proposed method effectively suppresses the influence of non-line-of-sight errors, has the advantages of low cost, high reliability, strong environment adaptability, and overcomes the shortcomings of traditional methods. © 2021, Science Press. All right reserved.
引用
收藏
页码：79 / 86
页数：7
相关论文
共 18 条
  • [11] NG H F, ZHANG G, HSU L T., A computation effective range-based 3D mapping aided GNSS with NLOS correction method[J], Journal of Navigation, 73, 6, pp. 1202-1222, (2020)
  • [12] BARRAL V, ESCUDERO C J, JA G N, Et al., NLOS identification and mitigation using low-cost UWB devices, Sensors, 19, 16, pp. 3464-3478, (2019)
  • [13] WYMEERSCH H., A machine learning approach to ranging error mitigation for UWB localization, IEEE Transactions on Communications, 60, 6, pp. 1719-1728, (2012)
  • [14] JIANG C, SHEN J, CHEN S, Et al., UWB NLOS/LOS classification using deep learning method, IEEE Communications Letters, 24, 10, pp. 2226-2230, (2020)
  • [15] VARGAS A, MENEGAZ H, ISHIHARA J, Et al., Unscented Kalman filters for estimating the position of an automotive electronic throttle valve, IEEE Transactions on Vehicular Technology, 65, 6, pp. 4627-4632, (2016)
  • [16] ZHANG H, ZHANG Z., AOA-based three-dimensional positioning and tracking using the factor graph technique, Symmetry, 12, 9, pp. 1400-1415, (2020)
  • [17] KAESS M, JOHANNSSON H, ROBERTS R, Et al., iSAM2: Incremental smoothing and mapping using the Bayes tree, Int J Robot Res, 31, 2, pp. 216-235, (2012)
  • [18] LI X, CHEN W, CHAN C, Et al., Multi-sensor fusion methodology for enhanced land vehicle positioning[J], Information Fusion, 46, pp. 51-62, (2019)
12