卫星拒止环境下基于因子图的智能车可靠定位方法
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TH89 TN967. 2

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国家自然科学基金(61973079)项目资助


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

    针对如室内停车场、隧道等卫星拒止环境下智能车的准确可靠定位需求,提出了一种基于因子图的低成本融合定位方 法。 首先,构建基于因子图的 UWB/ INS 动态融合定位框架;接着,充分利用 UWB 基站的静止特性,结合因子图输出的位置及 位置置信区间计算智能车与基站之间的最大理论距离,并与 UWB 实测距离比较从而检测非视距信号;最后,制定自适应融合 规则来指导不同情况下 UWB/ INS 的融合,输出最终的定位结果,实现卫星拒止环境下智能车的可靠定位。 实车试验表明,所提 出的融合定位方法可达到 0. 622 m 的精度,相较传统的最小二乘定位方法提升 1 倍以上,有效抑制了非视距误差的影响,具有 成本低、可靠性高、环境适应力强等优点,克服了传统方法的不足。

    Abstract:

    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-ofsight 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.

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胡 悦,李 旭,徐启敏,袁建华,孔 鑫.卫星拒止环境下基于因子图的智能车可靠定位方法[J].仪器仪表学报,2021,(11):79-86

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  • 在线发布日期: 2023-06-28
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