This paper proposes a new approach for calibration of dead reckoning process. Using the well-known UMBmark (University of Michigan Benchmark) is not sufficient for a desirable calibration of dead reckoning. Besides, existing calibration methods usually require explicit measurement of actual motion of the robot. Some recent methods use the smart encoder trailer or long range finder sensors such ...

Obviously navigation is one of the most complicated issues in mobile robots. Intelligent algorithms are often used for error handling in robot navigation. This Paper deals with the problem of Inertial Measurement Unit (IMU) error handling by using Extended Kalman Filter (EKF) as an Expert Algorithms. Our focus is put on the field of mobile robot navigation in the 2D environments. The main chall...

the aim of this study is to investigate iranian tefl graduates’ conception of measurement error in research. adopting a sequential explanatory multi-method strategy (borg, 2009), the researchers analyzed causal and temporal relations in the research narratives elicited from 30 tefl graduates. gee’s (1986) framework for identifying narrative discourse units (lines, stanzas, and episodes) was ado...

The ordinary maximum likelihood (ML) approach in classical regression models, fails when the independent variables are subject to error. The most noticeable and well known problem reported in the literature is the inconsistency of the ML estimators [1]. To solve this problem, a number of alternatives were proposed. The measurement error model (MEM) is the most fashionable of them, but it has so...

the aim of this paper is to propose some diagnostic methods in linear ridge measurement error models with stochastic linear restrictions using the corrected likelihood. based on the bias-corrected estimation of model parameters, diagnostic measures are developed to identify outlying and influential observations. in addition, we derive the corrected score test statistic for outliers detection ba...

We consider functional measurement error models where the measurement error distribution is estimated non-parametrically.We derive a locally efficient semiparametric estimator but propose not to implement it owing to its numerical complexity. Instead, a plug-in estimator is proposed, where the measurement error distribution is estimated through non-parametric kernel methods based on multiple me...