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      A Track Geometry Measuring System Based on Multibody Kinematics, Inertial Sensors and Computer Vision

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          Abstract

          This paper describes the kinematics used for the calculation of track geometric irregularities of a new Track Geometry Measuring System (TGMS) to be installed in railway vehicles. The TGMS includes a computer for data acquisition and process, a set of sensors including an inertial measuring unit (IMU, 3D gyroscope and 3D accelerometer), two video cameras and an encoder. The kinematic description, that is borrowed from the multibody dynamics analysis of railway vehicles used in computer simulation codes, is used to calculate the relative motion between the vehicle and the track, and also for the computer vision system and its calibration. The multibody framework is thus used to find the formulas that are needed to calculate the track irregularities (gauge, cross-level, alignment and vertical profile) as a function of sensor data. The TGMS has been experimentally tested in a 1:10 scaled vehicle and track specifically designed for this investigation. The geometric irregularities of a 90 m-scale track have been measured with an alternative and accurate method and the results are compared with the results of the TGMS. Results show a good agreement between both methods of calculation of the geometric irregularities.

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          Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs

          Orientation estimation using low cost sensors is an important task for Micro Aerial Vehicles (MAVs) in order to obtain a good feedback for the attitude controller. The challenges come from the low accuracy and noisy data of the MicroElectroMechanical System (MEMS) technology, which is the basis of modern, miniaturized inertial sensors. In this article, we describe a novel approach to obtain an estimation of the orientation in quaternion form from the observations of gravity and magnetic field. Our approach provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. We separate the problems of finding the “tilt” quaternion and the heading quaternion in two sub-parts of our system. This procedure is the key for avoiding the impact of the magnetic disturbances on the roll and pitch components of the orientation when the sensor is surrounded by unwanted magnetic flux. We demonstrate the validity of our method first analytically and then empirically using simulated data. We propose a novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings. The correction part of the filter is based on the method described above and works for both IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. We evaluate the effectiveness of the filter and show that it significantly outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter.
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            Perspectives on railway track geometry condition monitoring from in-service railway vehicles

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              Estimation of IMU and MARG orientation using a gradient descent algorithm

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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                20 January 2021
                February 2021
                : 21
                : 3
                : 683
                Affiliations
                [1 ]Department of Mechanical and Manufacturing Engineering, University of Seville, 41092 Seville, Spain; purda@ 123456us.es
                [2 ]Department of Materials and Transportation Engineering, University of Seville, 41092 Seville, Spain; sergiomunoz@ 123456us.es
                Author notes
                [* ]Correspondence: escalona@ 123456us.es
                Author information
                https://orcid.org/0000-0002-5366-5802
                https://orcid.org/0000-0002-1132-8453
                https://orcid.org/0000-0002-6003-622X
                Article
                sensors-21-00683
                10.3390/s21030683
                7864017
                33498313
                2b0d3cee-f328-4f36-88de-b8db4b0d1048
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 22 December 2020
                : 15 January 2021
                Categories
                Article

                Biomedical engineering
                rail vehicles,track irregularities,multibody dynamics,inertial sensors,computer vision

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