摘要
在集群协同网络外源距离观测动态变化情况下,传统固定式卡尔曼滤波估计架构存在系统状态估计精度及拓扑式协同融合能力不足的问题。对此,结合因子图即插即用优化估计架构,提出了一种基于相对距离约束的异构集群网络拓扑式协同优化方法。根据复杂环境下异类异构导航传感器误差特性,构建无人车内源惯性预积分因子节点、里程计因子节点和相对距离约束因子节点,建立异构集群网络系统状态全局优化图模型。通过构建面向滑动窗口的无人车状态全局优化代价函数,利用高斯牛顿非线性最小二乘法实现系统状态量的全局优化估计。仿真及实验结果表明,所提方法能够实现协同网络异类异构导航信息源的拓扑式融合估计,相较于传统基于卡尔曼滤波架构的协同定位方法,卫星拒止环境下无人车定位精度提高了50%以上。
In the case of dynamic topology of external distance observation in cluster cooperative network,estimation accuracy of system state and topological collaborative fusion ability are insufficient using traditional fixed Kalman filter estimation architecture.Combined with the factor graph plug and play optimization estimation architecture,a topology collaborative optimization method for heterogeneous cluster network based on relative distance constraint is proposed.Based on the error characteristics of the heterogeneous navigation sensors in complex environment,the internal inertial pre-integration factor node,odometer factor node and external relative distance constraint factor node of unmanned ground vehicle(UGV)are derived and constructed.The global optimization graph model of heterogeneous cluster network system state is established.By constructing the sliding window-oriented global optimization cost function of UGV’s state,Gaussian Newton nonlinear least square method is employed to achieve global optimization estimation of system state variables.The simulation and experimental results show that the proposed method achieves topological fusion estimation of the heterogeneous navigation information sources in the cluster cooperative network.Compared to the traditional cooperative positioning method based on the Kalman filtering architecture,the proposed method improves the positioning accuracy of UGV by more than 50%in satellite denial environment.
作者
韩世东
熊智
史晨发
杨杰
HAN Shidong;XIONG Zhi;SHI Chenfa;YANG Jie(Navigation Research Center,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;Nanjing Vocational Institute of Railway Technology,Nanjing 210031,China)
出处
《中国惯性技术学报》
北大核心
2025年第6期567-576,共10页
Journal of Chinese Inertial Technology
基金
国家自然科学基金(62073163,62103285,62203228)
江苏轨道交通产业发展协同创新基地开放基金(GCXC2402)。
关键词
卫星拒止
异构机器人
协同定位
相对距离
协同优化
satellite denial
heterogeneous robots
cooperative localization
relative distance
collaborative optimization
