南京航空航天大学学报（英文版）2024，Vol.41Issue(z1)：43-51,9.DOI:10.16356/j.1005-1120.2024.S.006
基于因子图优化的无人系统GNSS/INS/视觉多传感器融合状态估计方法
State Estimation Method for GNSS/INS/Visual Multi-sensor Fusion Based on Factor Graph Optimization for Unmanned System
摘要
Abstract
With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation satellite system/inertial navigation system(GNSS/INS)integrated navigation systems can provide high-precision navigation information continuously.However,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 data.The positioning and orientation errors will diverge and accumulate rapidly,which cannot meet the high-precision localization requirements in large-scale and long-distance navigation scenarios.This 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 optimization.Through the collected experimental data and simulation results,this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.关键词
状态估计/多传感器融合/组合导航/因子图优化/复杂环境Key words
state estimation/multi-sensor fusion/combined navigation/factor graph optimization/complex environments分类
航空航天引用本文复制引用
朱泽堃,杨忠,薛八阳,张驰,杨欣..基于因子图优化的无人系统GNSS/INS/视觉多传感器融合状态估计方法[J].南京航空航天大学学报（英文版）,2024,41(z1):43-51,9.基金项目
The work was supported in part by the Guangxi Power Grid Company's 2023 Science and Technol-ogy Innovation Project(No.GXKJXM20230169). （No.GXKJXM20230169）
