State Estimation Method for GNSS/INS/Visual Multi-sensor Fusion Based on Factor Graph Optimization for Unmanned System

作     者：ZHU Zekun YANG Zhong XUE Bayang ZHANG Chi YANG Xin 朱泽堃;杨忠;薛八阳;张驰;杨欣

作者机构：College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China 

出 版 物：《Transactions of Nanjing University of Aeronautics and Astronautics》 (南京航空航天大学学报（英文版）)

年 卷 期：2024年第41卷第S01期

页      面：43-51页

核心收录：

学科分类：08[工学] 081105[工学-导航、制导与控制] 0811[工学-控制科学与工程] 

基　　金：supported in part by the Guangxi Power Grid Company’s 2023 Science and Technol-ogy Innovation Project(No.GXKJXM20230169) 

主　　题：state estimation multi-sensor fusion combined navigation factor graph optimization complex environments 

摘      要：With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great *** global navigation satellite system/inertial navigation system(GNSS/INS)integrated navigation systems can provide high-precision navigation information ***,when this system is applied to indoor or GNSS-denied environments,such as outdoor substations with strong electromagnetic interference and complex dense spaces,it is often unable to obtain high-precision GNSS positioning *** positioning and orientation errors will diverge and accumulate rapidly,which cannot meet the high-precision localization requirements in large-scale and long-distance navigation *** paper proposes a method of high-precision state estimation with fusion of GNSS/INS/Vision using a nonlinear optimizer factor graph optimization as the basis for multi-source *** the collected experimental data and simulation results,this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.