INS/GNSS/UWB/OD Robust Navigation Algorithm Based on Factor Graph

Yuliang Jin, Siyuan Liu, Fei Yu, Ya Zhang, Shiwei Fan
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Abstract

Multi-source navigation system can complete cross-scene and high-precision navigation tasks, and its robustness is a research focus in this field. In this paper, a robust INS/GNSS/UWB/OD navigation algorithm based on factor graph is proposed for unmanned ground vehicle (UGV). The sum-product algorithm is used to complete multi-sensor information fusion. In order to ensure that the algorithm can still have strong robustness in various complex environments, this paper designs a regulation factor by using residual error and its covariance, adaptively adjusts the measurement covariance matrix, so that the navigation system can still achieve high precision navigation when the accuracy of the sensor is reduced due to occlusion or faults. In order to evaluate the robust navigation algorithm proposed in this paper, on-vehicle experiments were carried out under different conditions. The results show that the proposed algorithm significantly improves the robustness and accuracy of the multi-source navigation system.
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基于因子图的INS/GNSS/UWB/OD鲁棒导航算法
多源导航系统能够完成跨场景、高精度的导航任务,其鲁棒性是该领域的研究热点。针对无人地面车辆,提出了一种基于因子图的稳健INS/GNSS/UWB/OD导航算法。采用和积算法完成多传感器信息融合。为了保证算法在各种复杂环境下仍然具有较强的鲁棒性,本文利用残差及其协方差设计调节因子,自适应调整测量协方差矩阵,使导航系统在传感器因遮挡或故障导致精度降低时仍能实现高精度导航。为了验证本文提出的鲁棒导航算法,在不同条件下进行了车载实验。结果表明,该算法显著提高了多源导航系统的鲁棒性和精度。
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