基于改进粒子群优化算法的非线性摄像机标定

本文针对传统优化算法在摄像机标定中存在对初始值敏感、收敛性差、易陷入局部最优等缺点,研究了粒子群优化算法在非线性摄像机标定中的应用,给出了利用改进粒子群 优化算法进行摄像机参数标定的具体步骤。标定实验表明,基于该算法的摄像机标定方法可以克服传统算法的不足,是一种有效的标定方法。     

基于自适应布谷鸟算法的摄像机标定方法研究

该文将自适应布谷鸟算法应用于摄像机标定中,解决传统摄像机标定算法参数多、计算量大等问题。通过对参数进行布谷鸟算法优化,解决群个体陷入局部最优的问题,增强算法全局寻优能力,从而提高布谷鸟算法的多样性。实验结果表明,该算法具有较好的全局搜索能力和避免局部最优的能力,可以准确地完成摄像机标定,在收敛精度、速度以及鲁棒性等性能上显著优于遗传算法、差分进化算法、基本布谷鸟搜索算法。     

直线运动摄像机在线动态标定

为实现AS-R智能机器人在运动情况下摄像机在线动态标定,提出一种新的基于粒子滤波的直线运动摄像机标定方法。用状态空间方法描述直线运动摄像机模型,把摄像机内参数和位置运动参数作为状态量,特征点图像坐标作为观测量,根据粒子滤波算法求得摄像机内参数和位置运动参数的最优估计,并用双线程实现整个标定过程。AS-R机器人在直线运动情况下的摄像机在线动态标定实验结果表明：该算法是合理可行的,并且具有很高的标定精度和良好的鲁棒性。该方法适用于各种类型的系统噪声。     

基于PO的分步高精度相机参数标定优化算法

相机标定是为了解决计算机视觉中2D图像特征和相应的3D特征之间的映射关系。针对精确标定的困难,提出了一种基于政治优化器(Political Optimizer, PO)的分步标定算法来分步求解相机几何参数和镜头畸变系数,采用参数标定与畸变修正相结合的方法,首先在理想状态下对相机参数进行寻优,其次在考虑畸变的情况下修正畸变系数,通过迭代的方式更新个体的位置来找到最佳的解,有效地校准了相机。实验结果表明,PO算法的平均误差为0.044 706像素,具有良好的避免局部最优的优化能力,能够准确完成相机标定任务。     

凸松弛全局优化机器人手眼标定

针对机器人运动学正解及相机的外参数标定存在偏差时,基于非线性最优化的手眼标定算法无法确保目标函数收敛到全局极小值的问题,提出基于四元数理论的凸松弛全局最优化手眼标定算法。考虑到机械手末端相对运动旋转轴之间的夹角对标定方程求解精度的影响,首先利用随机抽样一致性（RANSAC）算法对标定数据中旋转轴之间的夹角进行预筛选,再利用四元数参数化旋转矩阵,建立多项式几何误差目标函数和约束,采用基于线性矩阵不等式（LMI）凸松弛全局优化算法求解全局最优手眼变换矩阵。实测结果表明,该算法可以求得全局最优解,手眼变换矩阵几何误差平均值不大于1.4 mm,标准差小于0.16 mm,结果稍优于四元数非线性最优化算法。     

捷联惯导系统误差系数动态标定方法探究

随着工作时间的推移,捷联惯性导航系统中的惯性器件测量精度会因为误差参数发生变化而降低,误差逐渐被积累,进而降低系统导航精度。针对此问题,提出了一种基于GPS速度、位置信息的捷联惯导系统惯性测量装置输出误差系数动态标定的方法。首先采用Sage-Husa自适应滤波实现组合导航状态最优估计,然后引入迭代最小二乘法,利用导航误差对系统惯性器件的误差系数进行标定。经计算机仿真验证,该方法可以实现在运动中对系统误差系数进行标定,进而提高捷联式惯性导航系统的导航精度。     

摄像机标定方法的建模与仿真研究

研究摄像机定位优化控制问题,摄像机镜头存在多种非线性畸变,对标定路径和图像质量产生一定的影响,难以采用精确数学模型来描述,针对传统方法的摄像机标定准确率低。为了提高摄像机的标定准确率,利用LSSVM较好的处理非线性预测能力,建立一种PSO-LSSVM的摄像机标定模型。将摄像头采集到的图像坐标作为输入,将世界坐标作为输出,通过采用LSSVM精确逼近输入与输出的复杂非线性关系,采用PSO寻找LSSVM最优参数,提高标定准确率。通过标定模型进行对比实验,实验结果表明,PSO-LSSVM不仅加快标定速度,且提高了摄像机标定的准确率。     

基于改进遗传算法的摄像机自标定方法

摄像机自标定技术不受标定板和相机运动轨迹的限制,其标定过程简单、适用性强。由于传统的遗传算法在摄像机自标定参数优化过程中易出现过早收敛、停滞现象和解易陷入局部最优的问题,提出一种改进的遗传算法。首先,通过结合精英保留策略和随机联赛选择算法作为初始化种群的方法、改进轮盘赌选择方法、采用自适应杂交概率和变异概率方法对遗传算法进行改进;然后,将Hartley定义的简化Kruppa方程转化为目标函数,采用改进的遗传算法搜索目标函数的最优值;最后,实验结果表明,该方法能较好地缓解过早收敛和停滞显现,提高了精度。     

基于多约束误差函数的2维激光雷达和摄像机标定方法

基于棋盘格标定板的标定方法是当今主流的2维激光雷达和摄像机标定方法(以2004年Zhang提出的方法最为经典),但是该方法在优化标定参数时使用的误差函数是弱约束关系,影响了标定结果的精确度和稳定性.由于激光点不但应该位于标定板平面上而且应该位于标定板的矩形区域内部,因此基于以上两个空间约束关系本文提出一种多约束误差函数模型.与原有的Zhang方法比较,所提方法可在更严格的可行域空间中搜索最优值,实验结果定性和定量地显示本文方法提高了标定结果的精确度和鲁棒性.     

机器人立体视觉中摄像机的标定

针对机器人立体视觉标定问题,提出一种介于传统标定法和自标定法之间的摄像机标定方法。与传统方法相比,将内参数、畸变参数与外参数分别标定。首先根据张氏平面标定法求出内参数;其次在考虑透镜径向畸变的基础上求出畸变参数;最后重点探讨在摄像机带有运动的情况下外参数的标定方法,避免了在原有标定环境下摄像机运动时需要重新标定的问题。实验依据传统标定的模板原理,结合双目立体视觉模型,验证了该种分离标定方法的可行性。     

An exact sequential solution procedure for a class of discrete-time nonlinear estimation problems

An exact procedure is developed for sequentially updating the optimal solution for a general discrete-time nonlinear least-squares estimation problem as the process length increases and new observations are obtained. The optimal sequential estimation equations are derived by means of an imbedding on two physically meaningful parameters, namely, the duration of the dynamical process and the value of the final observation. The optimal sequential estimation equations are contrasted with the approximate sequential estimation equations which would be obtained via extended Kalman filtering.     

A recursive algorithm for ARMAX model identification in closed loop

The joint problem of the recursive estimation of an optimal predictor for the closed-loop system and the unbiased parameter estimation of an ARMAX plant model in closed-loop operation is considered. A special reparameterized optimal predictor for the closed-loop is introduced. This allows a parameter estimation algorithm for the plant model to be derived which is globally asymptotically stable in a deterministic environment and gives asymptotically unbiased parameters estimates under richness conditions     

An estimation problem for linear stochastic equations with memory

We consider optimal estimation of solutions of linear equations with memory. The observation process contains a delay. Expressions are derived for the optimal estimate and the estimation error. Their dependence on the problem parameters is analyzed.Translated from Kibernetika, No. 3, pp. 76–80, May–June 1990.     

Convergence of continuous-time partitioned adaptive state estimators

The asymptotic behaviour of Bayes optimal adaptive state estimation schemes (also called the partitioned adaptive estimation algorithms) for continuous-time linear dynamic Gauss-Markov systems with unknown parameters is investigated. The unknown system parameters are asssumed to belong to a finite set. The results are developed through, weak consistency of the maximum likelihood and the maximum a posteriori probability estimates of the unknown parameters.     

Specific optimal estimation

The goal of the specific optimal estimation problem is to achieve near-optimal (minimum variance) estimation using a structure that is easier to implement than the optimal solution. One chooses a reasonable configuration for the filter in which certain parameters are unspecified and then selects the parameters so that its performance is optimized. The problem is formulated as a two-point boundary-value problem resulting from consideration of the covariance of error of the estimate and application of the matrix-minimum principle. The examples presented indicate that near-optimal results can be obtained using a filter designed in this way.     

多模型系统基于黄金分割法的参数估计

利用变点统计学和黄金分割法讨论具有跳变参数的多模型离散线性动态系统的参数估计。提出基于黄金分割法搜索最佳变点估计（参数变化时间）和同时得到参数估计的最小二乘算法。理论分析和计算机仿真结果表明，该算法具有较好的收敛性并能得到良好的变点及参数的估计值。     

To the optimal identification of multivariate systems under perturbations of unknown covariances

We consider the problem of parameter and covariance estimation for multivariate stochastic systems described by regression models with special structure perturbations of unknown covariance. Sufficient conditions of uniformly optimal estimations are obtained for system parameters and covariances. The observation vector distribution family is factorized, and the full sufficient statistics is found under those conditions. Equations for uniformely optimal unbiased estimates of covariance parameters are obtained.     

Problems of Adaptive Optimal Control of Discrete-Time Systems under Bounded Disturbance and Linear Performance Indexes

We present some problems of adaptive optimal robust control of linear discretetime systems under uncertainty and bounded external disturbance in which the optimal or guaranteed value of the performance index is a linear or linear-fractional function of unknown parameters of the system and set-membership estimation based on the method of recurrent objective inequalities reduces to recurrent updating of polyhedral estimates of unknown parameters. In such problems, computing current optimal estimates becomes a recurrent linear programming problem which is computationally tractable on modern computers for systems with a small number of estimated parameters.     

An approach to the selection of optimal sensor locations in distributed parameter systems

This paper presents an approach to the selection of optimal sensor locations in distributed parameter systems, which distinguishes the purposes of state estimation from the purposes of parameter estimation. In the first case, the optimality criterion is based on a measure of independence between the sensor responses, while in the second case, it is based on a measure of independence between the parameter sensitivity functions. The procedure, which is general and can be applied to models with any degree of complexity, is illustrated with the optimal placement of temperature sensors in a catalytic fixed-bed reactor. Some numerical results for the on-line estimation of temperature and concentration profiles as well as for the estimation of unknown model parameters are discussed.     

Optimal inputs and sensitivites for nonlinear process parameter estimation using imbedding techniques

It has been shown [2], [3] that for parameter estimation of a dynamical system with a limited number of measurements, the use of inputs maximizing the sensitivity of the states with respect to the parameters increases the accuracy of the estimation. In this correspondence the continuation method is used both for determining the optimal inputs and estimating the unknown parameters. A simple example demonstrates the approach.