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速发散和积累,无法满足大规模和长距离导航场景中的高精度定位要求.本文提出了一种基于非线性因子图优化的GNSS/INS/视觉融合的高精度状态估计方法.通过收集的实验数据和仿真结果,该系统在室内环境和部分GNSS信号丢失的环境中表现良好.
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.
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
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 (0z1)
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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