This paper presents a new parameterization approach for the graph-based SLAM problem utilising unit dual-quaternion. The rigid-body transformation typically consists of the robot position and rotation, and due to the ...
详细信息
ISBN:
(纸本)9781467379717
This paper presents a new parameterization approach for the graph-based SLAM problem utilising unit dual-quaternion. The rigid-body transformation typically consists of the robot position and rotation, and due to the Lie-group nature of the rotation, a homogeneous transformation matrix has been widely used in pose-graph optimizations. In this paper, we investigate the use of unit dual-quaternion for SLAM problem, providing a unified representation of the robot poses with computational and storage benefits. Although unit dual-quaternion has been widely used in robot kinematics and navigation (known also as Michel Chasles' theorem), it has not been well utilised in the graph SLAM optimization. In this work, we re-parameterize the graph SLAM problem with dual-quaternions, investigating the optimization performance and the sensitivity to poor initial estimates. Experimental results on public synthetic and real-world datasets show that the proposed approach significantly reduces the computational complexity, whilst retaining the similar map accuracies compared to the homogeneous transform matrix-based one.
Sensitivity analysis is a useful tool to identify key model parameters as well as to quantify simulation errors resulting from parameter uncertainty. The Root Zone Water Quality Model (RZWQM) has been subjected to var...
详细信息
Sensitivity analysis is a useful tool to identify key model parameters as well as to quantify simulation errors resulting from parameter uncertainty. The Root Zone Water Quality Model (RZWQM) has been subjected to various sensitivity analyses;however, in most of these efforts a local sensitivity analysis method was implemented, the nonlinear response was neglected, and the dependency among parameters was not examined. In this study we employed a comprehensive global sensitivity analysis to quantify the contribution of 70 model input parameters (including 35 hydrological parameters and 35 nitrogen cycle parameters) on the uncertainty of key RZWQM outputs relevant to raspberry row crops in Abbotsford, BC, Canada. Specifically, 9 model outputs that capture various vertical-spatial and temporal domains were investigated. A rank transformation method was used to account for the nonlinear behavior of the model. The variance of the model outputs was decomposed into correlated and uncorrelated partial variances to provide insight into parameter dependency and interaction. The results showed that, in general, the field capacity (soil water content at - 33 kPa) in upper 30 cm of the soil horizon had the greatest contribution (>30%) to the estimate of the water flux and evapotranspiration uncertainty. The most influential parameters affecting the simulation of soil nitrate content, mineralization, denitrification, nitrate leaching and plant nitrogen uptake were the transient coefficient of fast to intermediate humus pool, the carbon to nitrogen ratio of the fast humus pool, the organic matter decay rate in fast humus pool, and field capacity. The correlated contribution to the model output uncertainty was <10% for the set of parameters investigated. The findings from this effort were utilized in two calibration case studies to demonstrate the utility of this global sensitivity analysis to reduce the risk of over-parameterization, and to identify the vertical location of observ
This paper builds a model which has two extensions over a standard VAR. The first of these is stochastic search variable selection, which is an automatic model selection device that allows coefficients in a possibly o...
详细信息
This paper builds a model which has two extensions over a standard VAR. The first of these is stochastic search variable selection, which is an automatic model selection device that allows coefficients in a possibly over-parameterized VAR to be set to zero. The second extension allows for an unknown number of structural breaks in the VAR parameters. We investigate the in-sample and forecasting performance of our model in an application involving a commonly-used US macroeconomic data set. In a recursive forecasting exercise, we find moderate improvements over a standard VAR, although most of these improvements are due to the use of stochastic search variable selection rather than to the inclusion of breaks. (C) 2009 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
We present in this paper an experimental testing for a new algorithm that calculates a controller's coefficients for output variance minimization related to Linear Time Invariant (LTI) Systems. The algorithm featu...
详细信息
ISBN:
(纸本)9781424458219;9781424458240
We present in this paper an experimental testing for a new algorithm that calculates a controller's coefficients for output variance minimization related to Linear Time Invariant (LTI) Systems. The algorithm features simplicity in calculation, generalization to minimal and non-minimal phase systems. An experiment of DC-motor velocity feedback control demonstrates robustness characteristics for the new developed controller based on the proposed algorithm. Results show that for an identified ARMAX model for the DC motor model without taking account the variance of the identified coefficients, the controller manage to achieve minimum variance.
in order to yield more flexible models, the Cox regression model, lambda(t;x) = lambda(0)(t) exp(beta x), has been generalized using different non-parametric model estimation techniques. One generalization is the rela...
详细信息
in order to yield more flexible models, the Cox regression model, lambda(t;x) = lambda(0)(t) exp(beta x), has been generalized using different non-parametric model estimation techniques. One generalization is the relaxation of log-linearity in x, lambda(t;x)=lambda(0)(t)exp[r(x)]. Another is the relaxation of the proportional hazards assumption, lambda(t;x)=lambda(0)(t)exp[beta(t)x]. These generalizations are typically considered independently of each other. We propose the product model, lambda(t;x)=lambda(0)(t) exp[beta(t)r(x)] which allows for joint estimation of both effects, and investigate its properties. The functions describing the time-dependent 1 (t) and non-linear r(x) effects are modelled simultaneously using regression splines and estimated by maximum partial likelihood. Likelihood ratio tests are proposed to compare alternative models. Simulations indicate that both the recovery of the shapes of the two functions and the size of the tests are reasonably accurate provided they are based on the correct model. By contrast, type I error rates may be highly inflated, and the estimates considerably biased, if the model is misspecified. Applications in cancer epidemiology illustrate how the product model may yield new insights about the role of prognostic factors. Copyright (c) 2006 John Wiley & Sons, Ltd.
Producers who manipulate and switch their reported crop-yields between separately insured units can increase their insurance indemnities substantially. A statistical model that identifies potential yield switching is ...
详细信息
Producers who manipulate and switch their reported crop-yields between separately insured units can increase their insurance indemnities substantially. A statistical model that identifies potential yield switching is developed. The unrestricted statistical model is singular and is identified by imposing a mixture of system-estimable and system-nonestimable restrictions. Lower bound estimates of yield-switching fraud incidence and costs are obtained by applying the model to 207,067 multiple unit producers who purchased crop insurance in 1998.
A systematic investigation of over-parameterized and under-parameterized formulations in the least-squares adjustment of linear models is performed in this paper. over-parameterization and under-parameterization are m...
详细信息
A systematic investigation of over-parameterized and under-parameterized formulations in the least-squares adjustment of linear models is performed in this paper. over-parameterization and under-parameterization are modeling effects that can often occur in the adjustment of geodetic data. The former refers to situations where new unknown parameters are added to an existing model in order to provide a more precise deterministic description for a given data set. Such an expansion may either correspond to a physically meaningful and necessary model improvement (e.g. due to the presence of unknown systematic errors in the input data) or to a fabricated data over-fitting through the inclusion of fictitious parametric terms in the mathematical model for the data adjustment. On the other hand, under-parameterization schemes emerge when the effects of existing systematic disturbances are omitted from the mathematical model that is employed for the data adjustment, thus causing a bias in the estimates for the remaining model parameters. The main focus of this study is the statistical accuracy of the estimated model parameters and the conditions under which it can be improved, either through an over-parameterized model formulation or through an under-parameterized model formulation.
暂无评论