Vehicle integrated navigation IMU mounting angles estimation method based on nonlinear optimization

被引:2
|
作者
Lin, Feifan [1 ,2 ,3 ]
Wang, Shiliang [1 ,2 ,3 ]
Chen, Yanping [1 ,2 ,3 ]
Zou, Mengqiang [1 ,2 ,3 ]
Peng, Hui [1 ,2 ,3 ]
Liu, Yu [1 ,2 ,3 ]
机构
[1] Chongqing Univ Posts & Telecommun, Sch Optoelect Engn, Chongqing 400065, Peoples R China
[2] Chongqing Univ Post & Telecommun, Chongqing Key Lab Autonomous Nav & Microsyst, Chongqing 400065, Peoples R China
[3] Chongqing Univ Post & Telecommun, Chongqing Engn Res Ctr Intelligent Sensing Technol, Chongqing 400065, Peoples R China
基金
中国国家自然科学基金;
关键词
IMU mounting angles; nonlinear optimization; land vehicle navigation; GNSS/INS integration;
D O I
10.1088/1361-6501/ad1369
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A vehicle integrated navigation system based on Global Navigation Satellite System (GNSS) aided Inertial Navigation System (INS) enhances its navigation accuracy by utilizing non-holonomic constraints and odometer velocity. However, the navigation accuracy of the system is compromised due to the challenges in the estimation of the inertial measurement unit (IMU) mounting angles relative to the host vehicle. This paper presents a method for estimating the IMU mounting angles of a vehicle that integrated navigation system through a nonlinear optimization. The proposed method firstly achieves the initial alignment of the vehicle integrated navigation system through a GNSS/INS loosely coupled algorithm. Then, the vehicle navigation algorithm is switched to the INS algorithm after collecting 100 satellite-fixed solutions. Finally, a nonlinear optimization objective function based on the difference between the satellite-fixed solutions and the INS integration results is constructed to solve for the IMU mounting angles. Experiments were conducted by using a Murata SCHA634 IMU on a land vehicle to validate the proposed method. This method has a reduction of approximately 30% in the three-dimensional positioning accuracy within 1 sigma error compared to that without mounting angles compensation, and a reduction of approximately 15% when compared with the traditional approach of compensating for IMU mounting angles using the error state Kalman filter method.
引用
收藏
页数:11
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