parameter calibration
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2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yanwu Zhai ◽  
Haibo Feng ◽  
Yili Fu

Purpose This paper aims to present a pipeline to progressively deal with the online external parameter calibration and estimator initialization of the Stereo-inertial measurement unit (IMU) system, which does not require any prior information and is suitable for system initialization in a variety of environments. Design/methodology/approach Before calibration and initialization, a modified stereo tracking method is adopted to obtain a motion pose, which provides prerequisites for the next three steps. Firstly, the authors align the pose obtained with the IMU measurements and linearly calculate the rough external parameters and gravity vector to provide initial values for the next optimization. Secondly, the authors fix the pose obtained by the vision and restore the external and inertial parameters of the system by optimizing the pre-integration of the IMU. Thirdly, the result of the previous step is used to perform visual-inertial joint optimization to further refine the external and inertial parameters. Findings The results of public data set experiments and actual experiments show that this method has better accuracy and robustness compared with the state of-the-art. Originality/value This method improves the accuracy of external parameters calibration and initialization and prevents the system from falling into a local minimum. Different from the traditional method of solving inertial navigation parameters separately, in this paper, all inertial navigation parameters are solved at one time, and the results of the previous step are used as the seed for the next optimization, and gradually solve the external inertial navigation parameters from coarse to fine, which avoids falling into a local minimum, reduces the number of iterations during optimization and improves the efficiency of the system.


Author(s):  
Yuqin Gao ◽  
Chencheng Zhao ◽  
Tong Zhou ◽  
Di Wu ◽  
Yue Liu

Author(s):  
Simone Göttlich ◽  
Claudia Totzeck

AbstractWe propose a neural network approach to model general interaction dynamics and an adjoint-based stochastic gradient descent algorithm to calibrate its parameters. The parameter calibration problem is considered as optimal control problem that is investigated from a theoretical and numerical point of view. We prove the existence of optimal controls, derive the corresponding first-order optimality system and formulate a stochastic gradient descent algorithm to identify parameters for given data sets. To validate the approach, we use real data sets from traffic and crowd dynamics to fit the parameters. The results are compared to forces corresponding to well-known interaction models such as the Lighthill–Whitham–Richards model for traffic and the social force model for crowd motion.


2021 ◽  
Vol 11 (21) ◽  
pp. 9946
Author(s):  
Sunbok Lee ◽  
Youn-Jeng Choi ◽  
Hyun-Song Kim

The ultimate goal of E-learning environments is to improve students’ learning. To achieve that goal, it is crucial to accurately measure students’ learning. In the field of educational measurement, it is well known that the key issue in the measurement of learning is to place test scores on a common metric. Despite the crucial role of a common metric in the measurement of learning, however, less attention has been paid to this important issue in E-learning studies. In this study, we propose to use fixed-parameter calibration (FPC) in an item response theory (IRT) framework to set up a common metric in E-learning environments. To demonstrate FPC, we used the data from the MOOC “Introduction to Psychology as a Science” offered through Coursera collaboratively by Georgia Institute of Technology (GIT) and Carnegie Mellon University (CMU) in 2013. Our analysis showed that the students’ learning gains were substantially different with and without FPC.


Author(s):  
Paul Hoffmann ◽  
Sebastian Moser ◽  
Corinna Kofler ◽  
Michael Nelhiebel ◽  
Daniel Tscharnuter ◽  
...  

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