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Sensitivity study: The effects of beacon location errors on a vehicle's position and attitude estimation for a vision-based navigation system.

机译：敏感性研究：基于视觉的导航系统中信标位置错误对车辆位置和姿态估计的影响。

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A sensitivity study is conducted for this thesis work where the effects of beacon location errors on the estimation of a vehicle's position and attitude are examined. Beacon location variation is introduced into the standard vision-based navigation (VISNAV) problem as zero-mean Gaussian noise. Two different simulations are carried out to study the effects of this noise on the estimation of a vehicle's position and attitude. The first simulation incorporates the Levenberg-Marquardt (LM) algorithm and the Monte Carlo method together to study the effects of beacon location variation on position and attitude estimation. The second simulation is time-based in nature and incorporates both the LM algorithm and the Gaussian Least Squares Differential-Correction (GLSDC) algorithm together to estimate both position and attitude for two different geometrical situations. A derivation of a measurement-error covariance matrix and incorporation of this covariance matrix into the estimation routine through the weighting matrix is presented in this thesis. The measurement-error covariance matrix accounts for the nonlinear noise addition of the beacon location variation for the measurement model of the VISNAV system.; Simulation results show that incorporating the measurement-error covariance matrix into the estimation routine improves the vehicle's position and attitude estimates for the Monte Carlo simulation and the time simulations. The study looks at the calculation of the measurement-error covariance matrix using the state estimates instead of the true values for the simulation. Results show that there are no significant differences with calculating the measurement-error covariance matrix using the state estimates or the true values. This valuable result shows that the measurement-error covariance matrix can be employed in the estimation algorithm for a real-time system where the vehicle's true position and attitude are not known and the only valuable information available is the state estimates.
机译：针对本文工作进行了敏感性研究，其中研究了信标位置误差对车辆位置和姿态估计的影响。信标位置变化以零均值高斯噪声的形式引入标准基于视觉的导航（VISNAV）问题。进行了两种不同的模拟来研究这种噪声对车辆位置和姿态估计的影响。第一次仿真将Levenberg-Marquardt（LM）算法和Monte Carlo方法结合在一起，以研究信标位置变化对位置和姿态估计的影响。第二个模拟本质上是基于时间的，并且将LM算法和高斯最小二乘差分校正（GLSDC）算法结合在一起，以估计两种不同几何情况的位置和姿态。本文提出了测量误差协方差矩阵的推导，并通过加权矩阵将该协方差矩阵并入估计程序。测量误差协方差矩阵考虑了VISNAV系统的测量模型中信标位置变化的非线性噪声相加。仿真结果表明，将测量误差协方差矩阵合并到估算例程中，可以改善针对蒙特卡洛仿真和时间仿真的车辆位置和姿态估算。该研究着眼于使用状态估计值代替仿真的真实值来计算测量误差协方差矩阵。结果表明，使用状态估计或真实值计算测量误差协方差矩阵没有显着差异。这一有价值的结果表明，测量误差协方差矩阵可用于实时系统的估计算法中，在该算法中，车辆的真实位置和姿态未知，唯一可用的有价值信息是状态估计。

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