具有非完整约束移动机器人的超宽带-惯导-里程计融合定位与能观测性分析

为解决现有超宽带-惯导组合定位系统在轮式移动机器人的定位精度低、依赖高精度IMU等问题，提出了一种采用误差状态卡尔曼滤波融合超宽带-惯导-里程计的定位算法，利用里程计的线速度测量和由非完整约束隐含的伪测量，提高了移动机器人的位置和姿态估计精度. 同时，对于由多传感器测量模型组成的非线性系统，通过基于李导数的能观性秩条件分析方法对该系统的能观测性进行了详细的理论分析与数学证明，得到了系统局部弱可观的条件，从而确定了系统状态可以被无偏估计所需要的测量输出以及控制输入. 仿真结果表明，在满足能观测性条件时，本文提出的方法能够有效地获得移动机器人较准确的六自由度位姿，且相比传统方法显著提升了定位精度.

UWB-IMU-odometer fusion localization and observability analysis for mobile robots with nonhol onomic constraints

To solve the problems of low positioning accuracy and dependence on high precision IMU of the existing UWB IMU positioning system for wheeled mobile robots, a localization algorithm using error state Kalman filter to integrate UWB IMU Odometer is proposed to improve the position and attitude estimation accuracy of mobile robots using linear velocity measurement of odometry and pseudo measurement implied by the nonholonomic constraints. Meanwhile, for the nonlinear system composed of the multi sensor measurement models, a detailed theoretical analysis and mathematical proof of the observability of the system is carried out by an observability rank condition analysis method based on the Lie derivative, and the conditions under which the system is locally weakly observable are concluded, which determines the required measurement outputs and control inputs for unbiased estimation of the system states. The simulation results show that when the observability conditions are satisfied, the state estimation approach proposed in this paper can effectively obtain the accurate 6 DOF poses of the mobile robot and significantly improve the positioning accuracy compared with the conventional methods.