A Perturbed Markovian process with state‐dependent increments and measurement uncertainty in degradation modeling

2021 ◽  
Author(s):  
M. Oumouni ◽  
F. Schoefs
Keyword(s):  
Measurement Uncertainty ◽  
Markovian Process ◽  
Degradation Modeling ◽  
State Dependent

scholarly journals Probabilistic pose estimation using a Bingham distribution-based linear filter

2018 ◽  
Vol 37 (13-14) ◽  
pp. 1610-1631 ◽  
Author(s):  
Rangaprasad Arun Srivatsan ◽  
Mengyun Xu ◽  
Nicolas Zevallos ◽  
Howie Choset
Keyword(s):  
Measurement Uncertainty ◽  
Pose Estimation ◽  
Estimation Methods ◽  
Linear Filter ◽  
Estimation Errors ◽  
Measurement Models ◽  
Bingham Distribution ◽  
State Dependent ◽  
Unit Quaternions ◽  
Dependent Measurement

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.

National primary standard machine of Shore D hardness scale for metals and Leeb hardness scales

2020 ◽  
pp. 6-10
Author(s):  
A.E. Aslanyan ◽  
E.G. Aslanyan ◽  
S.M. Gavrilkin ◽  
A.S. Doynikov ◽  
A.N. Shchipunov
Keyword(s):  
Measurement Uncertainty ◽  
Primary Standard ◽  
Metrological Characteristics ◽  
National Primary Standard ◽  
Hardness Scale ◽  
Leeb Hardness

The article presents the results of studies to improve the National primary standard machine for hardness of metals on the shore D scale GET 161-2001, which were performed in FSUE “VNIIFTRI” from 2016 to 2018 in accordance with the technical task of Rosstandart.The improvement was carried out in order to ensure the uniformity of hardness measurements on the Leeb scales. The created new parts of the primary standard machine, which are settings for reproducing hardness numbers on the Leeb scales, are considered. Metrological characteristics of the upgraded and adopted National primary standard machine (GET 161-2019) were investigated, the budget of measurement uncertainty was calculated for reproducing hardness numbers on the Leeb scales.

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