论文标题
使用不确定的IMU和视力测量值的特殊欧几里得组SE（3）上的非线性随机估计器
Nonlinear Stochastic Estimators on the Special Euclidean Group SE(3) using Uncertain IMU and Vision Measurements
论文作者
论文摘要
使用可用的不确定测量值提出了两个新型的鲁棒非线性随机全姿势（即态度和位置）估计器（3）。由此产生的估计量利用了确定性姿势估计器的基本结构，该结构采用了随机意义。与非线性确定性姿势估计值不同，提出的六个自由度（DOF）姿势估计值（DOF）姿势估计值认为群体速度向量被持续的偏置和高斯随机噪声污染，这与非线性确定性姿势估计量不同，该估计量忽略了估计器导数中的噪声分量。提出的估计器确保闭环误差信号是半全球均匀统一的，最终是在均方根上的界限。数值结果证明了所提出的估计器的效率和鲁棒性，这些结果测试了估计器，以针对与组速度和身体框架测量相关的高噪声和偏差以及大初始化误差。关键字：非线性随机姿势滤波器，姿势观察者，位置，态度，ITO，随机微分方程，布朗运动过程，适应性估计，特征，惯性测量单元，惯性视觉系统，6 Dof，imu，se（3），SO（3），SO（3），方向，方向，ganmark，landmark，gaussian，gaussian，噪声。
Two novel robust nonlinear stochastic full pose (i.e, attitude and position) estimators on the Special Euclidean Group SE(3) are proposed using the available uncertain measurements. The resulting estimators utilize the basic structure of the deterministic pose estimators adopting it to the stochastic sense. The proposed estimators for six degrees of freedom (DOF) pose estimations consider the group velocity vectors to be contaminated with constant bias and Gaussian random noise, unlike nonlinear deterministic pose estimators which disregard the noise component in the estimator derivations. The proposed estimators ensure that the closed loop error signals are semi-globally uniformly ultimately bounded in mean square. The efficiency and robustness of the proposed estimators are demonstrated by the numerical results which test the estimators against high levels of noise and bias associated with the group velocity and body-frame measurements and large initialization error. Keywords: Nonlinear stochastic pose filter, pose observer, position, attitude, Ito, stochastic differential equations, Brownian motion process, adaptive estimate, feature, inertial measurement unit, inertial vision system, 6 DOF, IMU, SE(3), SO(3), orientation, landmark, Gaussian, noise.
