Adaptive integrated navigation algorithm based on interacting multiple model factor graph optimization

被引：0

作者：

Zeng Q. ^{[1
]}

Wang S. ^{[1
]}

Li F. ^{[1
]}

Shao C. ^{[1
]}

机构：

[1] College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing

来源：

Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology | 2024年 / 32卷 / 04期

关键词：

complex environment; factor graph; interacting multiple model; vehicle navigation;

D O I：

10.13695/j.cnki.12-1222/o3.2024.04.004

中图分类号：

学科分类号：

摘要：

To solve the problem of poor positioning accuracy of traditional vehicle navigation system caused by external interference or sensor failure in complex urban environments, an adaptive integrated navigation algorithm based on interactive multiple model factor graph optimization is proposed. The IMU/GNSS/LIDAR integrated navigation system model is constructed based on the factor graph optimization algorithm. The interactive multiple model is applied in the modeling process of sensor measurements and constructing variable nodes. The model update probability is used to optimize the sensor weights and the solution and update of the vehicle navigation system is realized based on the nonlinear optimization and incremental smoothing theory of factor graph algorithm. The experimental results show that compared with the adaptive factor graph optimization algorithm, the proposed algorithm can improve the positioning accuracy of vehicle navigation system in complex urban environments by 26.2%. © 2024 Editorial Department of Journal of Chinese Inertial Technology. All rights reserved.

引用

页码：346 / 353

页数：7

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