Learning State-Dependent Sensor Measurement Models with Limited Sensor Measurements

Pose estimation is central to several robotics applications such as registration, hand–eye calibration, and simultaneous localization and mapping (SLAM). Online pose estimation methods typically use Gaussian distributions to describe the uncertainty in the pose parameters. Such a description can be inadequate when using parameters such as unit quaternions that are not unimodally distributed. A Bingham distribution can effectively model the uncertainty in unit quaternions, as it has antipodal symmetry, and is defined on a unit hypersphere. A combination of Gaussian and Bingham distributions is used to develop a truly linear filter that accurately estimates the distribution of the pose parameters. The linear filter, however, comes at the cost of state-dependent measurement uncertainty. Using results from stochastic theory, we show that the state-dependent measurement uncertainty can be evaluated exactly. To show the broad applicability of this approach, we derive linear measurement models for applications that use position, surface-normal, and pose measurements. Experiments assert that this approach is robust to initial estimation errors as well as sensor noise. Compared with state-of-the-art methods, our approach takes fewer iterations to converge onto the correct pose estimate. The efficacy of the formulation is illustrated with a number of examples on standard datasets as well as real-world experiments.

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Attitude Sensor Measurement Models

Fault Tolerant Attitude Estimation for Small Satellites ◽

10.1201/9781351248839-5 ◽

2020 ◽

pp. 53-65

Author(s):

Chingiz Hajiyev ◽

Halil Ersin Soken

Keyword(s):

Measurement Models ◽

Sensor Measurement ◽

Attitude Sensor

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FACT-2 – The Frankfurt Adaptive Concentration Test

European Journal of Psychological Assessment ◽

10.1027/1015-5759.25.2.73 ◽

2009 ◽

Vol 25 (2) ◽

pp. 73-82 ◽

Cited By ~ 9

Author(s):

Frank Goldhammer ◽

Helfried Moosbrugger ◽

Sabine A. Krawietz

Keyword(s):

Convergent Validity ◽

Four Dimensions ◽

Measurement Models ◽

Cognitive Failures ◽

Concentration Test ◽

Cognitive Failures Questionnaire

The Frankfurt Adaptive Concentration Test (FACT-2) requires discrimination between geometric target and nontarget items as quickly and accurately as possible. Three forms of the FACT-2 were constructed, namely FACT-I, FACT-S, and FACT-SR. The aim of the present study was to investigate the convergent validity of the FACT-SR with self-reported cognitive failures. The FACT-SR and the Cognitive Failures Questionnaire (CFQ) were completed by 191 participants. The measurement models confirmed the concentration performance, concentration accuracy, and concentration homogeneity dimensions of FACT-SR. The four dimensions of the CFQ (i.e., memory, distractibility, blunders, and names) were not confirmed. The results showed moderate convergent validity of concentration performance, concentration accuracy, and concentration homogeneity with two CFQ dimensions, namely memory and distractibility/blunders.

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Forced Zero Cross-Loading Misspecifications in Measurement Component of Structural Equation Models

Methodology ◽

10.1027/1614-2241/a000084 ◽

2014 ◽

Vol 10 (4) ◽

pp. 138-152 ◽

Cited By ~ 5

Author(s):

Hsien-Yuan Hsu ◽

Susan Troncoso Skidmore ◽

Yan Li ◽

Bruce Thompson

Keyword(s):

Structural Equation ◽

Structural Model ◽

Structural Equation Models ◽

Equation Model ◽

Model Fit ◽

Model Specification ◽

Simulation Design ◽

Measurement Models ◽

Fit Statistics ◽

Path Coefficients

The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.

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