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OACSTPCD
State Estimation Method for GNSS/INS/Visual Multi-sensor Fusion Based on Factor Graph Optimization for Unmanned System
随着无人驾驶技术、智能机器人和无人机的发展,高精度定位、导航和状态估计技术也取得了很大进步.传统的全球导航卫星/惯性(Global navigation satellite system/inertial navigation system,GNSS/INS)集成导航系统可以持续提供高精度的导航信息.然而,当该系统应用于室内或GNSS受限环境(如具有强电磁干扰和复杂密集空间的户外变电站)时,通常无法获得高精度的GNSS定位数据.定位和定向误差会迅…查看全部>>
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…查看全部>>
朱泽堃;杨忠;薛八阳;张驰;杨欣
南京航空航天大学自动化学院,南京 211106,中国南京航空航天大学自动化学院,南京 211106,中国南京航空航天大学自动化学院,南京 211106,中国南京航空航天大学自动化学院,南京 211106,中国南京航空航天大学自动化学院,南京 211106,中国
状态估计多传感器融合组合导航因子图优化复杂环境
state estimationmulti-sensor fusioncombined navigationfactor graph optimizationcomplex environments
《南京航空航天大学学报（英文版）》 2024 (z1)
43-51,9
The work was supported in part by the Guangxi Power Grid Company's 2023 Science and Technol-ogy Innovation Project(No.GXKJXM20230169).
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