Improved Scale-Factor and Bias Stability of Ovenized Inertial Sensors in an Environmentally-Stabilized Inertial Measurement Unit (eIMU)

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

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Systematic Calibration Method for FOG Inertial Measurement Units

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.662.717 ◽

2013 ◽

Vol 662 ◽

pp. 717-720 ◽

Cited By ~ 2

Author(s):

Zhen Yu Zheng ◽

Yan Bin Gao ◽

Kun Peng He

Keyword(s):

Inertial Measurement Unit ◽

Inertial Sensors ◽

Calibration Method ◽

State Equation ◽

Measurement Unit ◽

Observation System ◽

Body Frame ◽

Inertial Measurement ◽

Measurement Units ◽

Error Coefficients

As an inertial sensors assembly, the FOG inertial measurement unit (FIMU) must be calibrated before being used. The paper presents a one-time systematic IMU calibration method only using two-axis low precision turntable. First, the detail error model of inertial sensors using defined body frame is established. Then, only velocity taken as observation, system 33 state equation is established including the lever arm effects and nonlinear terms of scale factor error. The turntable experiments verify that the method can identify all the error coefficients of FIMU on low-precision two-axis turntable, after calibration the accuracy of navigation is improved.

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Feasibility Study of Multi Inertial Measurement Unit

Proceedings ◽

10.3390/ecsa-6-06582 ◽

2019 ◽

Vol 42 (1) ◽

pp. 74 ◽

Cited By ~ 1

Author(s):

Ariel Larey ◽

Eliel Aknin ◽

Itzik Klein

Keyword(s):

Inertial Measurement Unit ◽

Inertial Sensors ◽

Low Cost ◽

Software Tool ◽

Experimental Results ◽

Measurement Unit ◽

Outlier Rejection ◽

Inertial Measurement ◽

Navigation Solution ◽

Sensory Data

An inertial measurement unit (IMU) typically has three accelerometers and three gyroscopes. The output of those inertial sensors is used by an inertial navigation system to calculate the navigation solution–position, velocity and attitude. Since the sensor measurements contain noise, the navigation solution drifts over time. When considering low cost sensors, multiple IMUs can be used to improve the performance of a single unit. In this paper, we describe our designed 32 multi-IMU (MIMU) architecture and present experimental results using this system. To analyze the sensory data, a dedicated software tool, capable of addressing MIMUs inputs, was developed. Using the MIMU hardware and software tool we examined and evaluated the MIMUs for: (1) navigation solution accuracy (2) sensor outlier rejection (3) stationary calibration performance (4) coarse alignment accuracy and (5) the effect of different MIMUs locations in the architecture. Our experimental results show that 32 IMUs obtained better performance than a single IMU for all testcases examined. In addition, we show that performance was improved gradually as the number of IMUs was increased in the architecture.

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Wireless Input Technology Based on MEMS Inertial Measurement Unit - A Survey

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.870.79 ◽

2017 ◽

Vol 870 ◽

pp. 79-84

Author(s):

Zhen Xian Fu ◽

Guang Ying Zhang ◽

Yu Rong Lin ◽

Yang Liu

Keyword(s):

Inertial Measurement Unit ◽

Inertial Sensors ◽

Inertial Sensor ◽

Low Cost ◽

Propagation Model ◽

Measurement Unit ◽

Input Device ◽

Operational Environment ◽

Inertial Measurement ◽

Mems Imu

Rapid progress in Micro-Electromechanical System (MEMS) technique is making inertial sensors increasingly miniaturized, enabling it to be widely applied in people’s everyday life. Recent years, research and development of wireless input device based on MEMS inertial measurement unit (IMU) is receiving more and more attention. In this paper, a survey is made of the recent research on inertial pens based on MEMS-IMU. First, the advantage of IMU-based input is discussed, with comparison with other types of input systems. Then, based on the operation of an inertial pen, which can be roughly divided into four stages: motion sensing, error containment, feature extraction and recognition, various approaches employed to address the challenges facing each stage are introduced. Finally, while discussing the future prospect of the IMU-based input systems, it is suggested that the methods of autonomous and portable calibration of inertial sensor errors be further explored. The low-cost feature of an inertial pen makes it desirable that its calibration be carried out independently, rapidly, and portably. Meanwhile, some unique features of the operational environment of an inertial pen make it possible to simplify its error propagation model and expedite its calibration, making the technique more practically viable.

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Research on the Shearer Positioning Method Based on the MEMS Inertial Sensors/Odometer Integrated Navigation System and RTS Smoother

Micromachines ◽

10.3390/mi12121527 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1527

Author(s):

Jiangtao Zheng ◽

Sihai Li ◽

Shiming Liu ◽

Bofan Guan ◽

Dong Wei ◽

...

Keyword(s):

Inertial Measurement Unit ◽

Inertial Sensors ◽

Optimal Estimation ◽

Measurement Unit ◽

Integrated Navigation ◽

Estimation Model ◽

Positioning Accuracy ◽

Inertial Measurement ◽

Integrated Navigation System ◽

Positioning Method

The shearer positioning method with an inertial measurement unit and the odometer is feasible in the longwall coal-mining process. However, the positioning accuracy will continue to decrease, especially for the micro-electromechanical inertial measurement unit (MIMU). In order to further improve the positioning accuracy of the shearer without adding other external sensors, the positioning method of the Rauch-Tung-Striebel (RTS) smoother-aided MIMU and odometer is proposed. A Kalman filter (KF) with the velocity and position measurements, which are provided by the odometer and closing path optimal estimation model (CPOEM), respectively, is established. The observability analysis is discussed to study the possible conditions under which the error states of KF can be estimated. A RTS smoother with the above-mentioned KF as the forward filter is built. Finally, the experiments of simulating the movement of the shearer through a mobile carrier were carried out, with a longitudinal movement distance of 44.6 m and a lateral advance distance of 1.2 m. The results show that the proposed method can effectively improve the positioning accuracy. In addition, the odometer scale factor and mounting angles can be estimated in real time.

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ERROR ANALYSIS OF THE INERTIAL MEASUREMENT UNIT / INERCINIO MATAVIMŲ BLOKO KLAIDŲ ANALIZĖ

Aviation ◽

10.3846/16487788.2011.566311 ◽

2011 ◽

Vol 15 (1) ◽

pp. 5-10

Author(s):

František Adamcík

Keyword(s):

Signal Processing ◽

Error Analysis ◽

Kalman Filtering ◽

Inertial Measurement Unit ◽

Inertial Sensors ◽

Allan Variance ◽

Measurement Unit ◽

Inertial Measurement ◽

Navigation Solution ◽

Sensor Errors

The paper describes the results of error analysis of the new inertial measurement unit ADIS16364 produced by Analog Devices. This error analysis concerns stochastic sensor errors identified by the Allan variance method. In order to improve the performance of the inertial sensors, the users are keen to know more details about the noise components in each axis for a better modelling of the stochastic parts to improve the navigation solution. The main contribution of this paper is to present data necessary for further inertial sensors signal processing by means of Kalman filtering. Santrauka Straipsnyje pateikiami naujojo inercinio matavimų bloko ADIS16364 klaidų analizės rezultatai. Aprašyta klaidų analizė yra susijusi su stochastinio jutiklio klaidomis, nustatytomis Allano variacijos metodu. Siekiant pagerinti inercinių jutiklių našumą, naudotojai yra linkę daugiau sužinoti apie kiekvienoje ašyje esančius komponentus, kad būtų pagerintas stochastinių dalių modeliavimas bei rasti pažangesni navigacijos sprendimai. Šiuo darbu siekiama pristatyti duomenis, kurie yra reikalingi tolimesniam inercinių jutiklių signalų apdorojimui panaudojant Kalmano filtravimą.

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Deformation Measuring Methods Based on Inertial Sensors for Airborne Distributed POS

International Journal of Aerospace Engineering ◽

10.1155/2017/9343215 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 4

Author(s):

Xiaolin Gong ◽

Haojie Liu ◽

Xing-Gang Yan

Keyword(s):

Inertial Measurement Unit ◽

Inertial Sensors ◽

Estimation Accuracy ◽

Measurement Unit ◽

Practical Application ◽

Key Factors ◽

Inertial Measurement ◽

Measuring Methods ◽

Simulation Based ◽

Orientation System

This paper is focused on deformation measuring methods based on inertial sensors, which are used to achieve high accuracy motion parameters and the spatial distribution optimization of multiple slave systems in the airborne distributed Position and Orientation System or other purposes. In practical application, the installation difficulty, cost, and accuracy of measuring equipment are the key factors that need to be considered synthetically. Motivated by these, deformation measuring methods based on gyros and accelerometers are proposed, respectively, and compared with the traditional method based on the inertial measurement unit (IMU). The mathematical models of these proposed methods are built, and the detailed derivations of them are given. Based on the Kalman filtering estimation, simulation and semiphysical simulation based on vehicle experiment show that the method based on gyros can obtain a similar estimation accuracy to the method based on IMU, and the method based on accelerometers has an advantage in y-axis deformation estimation.

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Positioning Using Terrestrial Multipath Signals and Inertial Sensors

Mobile Information Systems ◽

10.1155/2017/9170746 ◽

2017 ◽

Vol 2017 ◽

pp. 1-18 ◽

Cited By ~ 10

Author(s):

Christian Gentner ◽

Robert Pöhlmann ◽

Markus Ulmschneider ◽

Thomas Jost ◽

Siwei Zhang

Keyword(s):

Particle Filtering ◽

Inertial Measurement Unit ◽

Inertial Sensors ◽

Multipath Propagation ◽

Position Estimation ◽

Measurement Unit ◽

Angle Of Arrival ◽

Inertial Measurement ◽

Multipath Signals ◽

Mobile Receiver

This paper extends an algorithm that exploits multipath propagation for position estimation of mobile receivers named Channel-SLAM. Channel-SLAM treats multipath components (MPCs) as signals from virtual transmitters (VTs) and estimates the positions of the VTs simultaneously with the mobile receiver positions. For Channel-SLAM it is essential to obtain angle of arrival (AoA) measurements for each MPC in order to estimate the VT positions. In this paper, we propose a novel Channel-SLAM implementation based on particle filtering which fuses heading information of an inertial measurement unit (IMU) to omit AoA measurements and to improve the position accuracy. Interpreting all MPCs as signals originated from VTs, Channel-SLAM enables positioning also in non-line-of-sight situations. Furthermore, we propose a method to dynamically adapt the number of particles which significantly reduces the computational complexity. A posterior Cramér-Rao lower bound for Channel-SLAM is derived which incorporates the heading information of the inertial measurement unit (IMU). We evaluate the proposed algorithm based on measurements with a single fixed transmitter and a moving pedestrian carrying the receiver and the IMU. The evaluations show that accurate position estimation is possible without the knowledge of the physical transmitter position by exploiting MPCs and the heading information of an IMU.

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A Comprehensive Overview of Inertial Sensor Calibration Techniques

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4034419 ◽

2016 ◽

Vol 139 (1) ◽

Cited By ~ 14

Author(s):

Shashi Poddar ◽

Vipan Kumar ◽

Amod Kumar

Keyword(s):

High Precision ◽

Kalman Filtering ◽

Inertial Measurement Unit ◽

Inertial Sensors ◽

Inertial Sensor ◽

Measurement Unit ◽

Sensor Calibration ◽

Comprehensive Overview ◽

Inertial Measurement ◽

Calibration Techniques

Inertial measurement unit (IMU) comprising of the accelerometer and gyroscope is prone to various deterministic errors like bias, scale factor, and nonorthogonality, which need to be calibrated carefully. In this paper, a survey has been carried out over different calibration techniques that try to estimate these error parameters. These calibration schemes are discussed under two broad categories, that is, calibration with high-end equipment and without any equipment. Traditional calibration techniques use high-precision equipment to generate references for calibrating inertial sensors and are generally laboratory-based setup. Inertial sensor calibration without the use of any costly equipment is further studied under two subcategories: ones based on multiposition method and others with Kalman filtering framework. Later, a brief review of vision-based inertial sensor calibration schemes is also provided in this work followed by a discussion which indicates different shortcomings and future scopes in the area of inertial sensor calibration.

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Low Cost Inertial Measurement Unit Application on Sub-Sea Launching: Signal Processing and Trajectory Algorithm

29th International Conference on Ocean, Offshore and Arctic Engineering: Volume 1 ◽

10.1115/omae2010-20857 ◽

2010 ◽

Author(s):

Rodrigo Sauri Lavieri ◽

Eduardo Aoun Tannuri ◽

Andre´ L. C. Fujarra ◽

Celso P. Pesce ◽

Diego Cascelli Correˆa

Keyword(s):

Automobile Industry ◽

Inertial Measurement Unit ◽

Inertial Sensors ◽

The Body ◽

Measurement Unit ◽

Final Position ◽

Test Procedures ◽

Positioning Systems ◽

Inertial Measurement ◽

Offshore Industry

Many situations in the Offshore Industry require equipment to be launched to the sea floor, becoming important to measure or to estimate their final position and/or to determine the complete trajectory. Some examples are the installation of anchorage devices, manifolds or production line supports. The main problem associated with the estimation of the position and the trajectory of the equipment is related to the fact that, systems such as GPSs and magnetometers cannot be used in subsea conditions. Gyrocompass and precise inertial sensors can be used, but they are expensive equipments and there is the risk of damaging during the launch process. The solution is to develop cost-effective inertial positioning systems that reach the operational requirements related to measuring accuracy. These equipments are based on MEMS (Micro-Electrical Mechanical Systems) inertial sensors that are relatively cheap. However, without the proper care, the signals obtained by these equipments present large levels of noise, bias and poor repeatability. The aim is to show a sequence of test procedures, treatment and processing of signals that leads one to know the position, attitude and trajectory of a submarine device. Furthermore, it allows the quantification of errors and, eventually, their sources. A commercial IMU (Inertial Measurement Unit) was chosen as a case study. It is equipped with MEMS sensors, usually adopted by the automobile industry. Tests with IMU were carried out intending to find the sensors scale factors, their bias and temperature sensitivity. Thereafter, the data were processed by two distinct algorithms. The first one is a simple algorithm that computes the attitude, azimuth at the final position and calculates the terminal velocity during the launch. The second one integrates the signal along all the movement by using quaternions algebra, resulting in the complete trajectory of the body. Discussions about the accuracy, applicability and limitations of each method are presented.

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