Gyro-free Inertial Navigation System Research Articles

Multiple and Gyro-Free Inertial Datasets

An inertial navigation system (INS) utilizes three orthogonal accelerometers and gyroscopes to determine platform position, velocity, and orientation. There are countless applications for INS, including robotics, autonomous platforms, and the internet of things. Recent research explores the integration of data-driven methods with INS, highlighting significant innovations, improving accuracy and efficiency. Despite the growing interest in this field and the availability of INS datasets, no datasets are available for gyro-free INS (GFINS) and multiple inertial measurement unit (MIMU) architectures. To fill this gap and to stimulate further research in this field, we designed and recorded GFINS and MIMU datasets using 54 inertial sensors grouped in nine inertial measurement units. These sensors can be used to define and evaluate different types of MIMU and GFINS architectures. The inertial sensors were arranged in three different sensor configurations and mounted on a mobile robot, a passenger car and a turntable. In total, the dataset contains 45 hours of inertial data and corresponding ground truth trajectories. The data is freely accessible through our figshare repository.

Transfer Alignment Design Using a Gyro-free Inertial Navigation System

Transfer Alignment Design Using a Gyro-free Inertial Navigation System

자이로스코프를 이용한 Global Positioning System/Gyro-Free Inertial Navigation System 통합항법 시스템의 성능향상

In this paper, the performance improvement of the global positioning system (GPS)/gyro-free inertial navigation system (GF-INS) integrated navigation system using gyroscopes is studied. Two methods are investigated: the addition of gyroscopes as aiding sensors and directly using gyroscopes for the INS. Navigation equations, error models, and Kalman filters are developed for both methods. Computer simulations are performed, and the results are compared with those of the GPS/GF-INS integrated navigation system. The results demonstrated that the proposed methods improve the navigation performance.

Note on Coarse Alignment of Gyro-Free Inertial Navigation System

In this note, the feasibility of initial alignment of a gyro-free inertial navigation system (GF-INS) is investigated. Initial roll and initial pitch are obtained using leveling of conventional INS since centripetal acceleration is very small. The equation for the initial heading cannot be used since the GF inertial measurement unit (IMU) cannot directly measure the Earth rate. A new equation is derived to obtain the initial heading from GF-IMU accelerometer outputs. Initial heading is expressed in the accelerometer outputs of two configurations, which satisfies a specific condition among 15 GF-IMU configurations presented in the literature. The initial heading error to arrangement and accelerometer error is quantitatively analyzed from the initial heading calculation equation of GF-INS and the initial heading error analysis of the general INS. The initial heading error is investigated when gyroscopes are used with GF-IMU. The results show that the initial heading error depends more on the performance of the gyroscope than that of the accelerometer, and the initial heading cannot be obtained within a practical error level by using only GF-IMU, even when an extremely accurate accelerometer is used. Therefore, aiding sensors have to be used in order to have a practical initial heading.

Gyro-Free Inertial Navigation Systems Based on Linear Opto-Mechanical Accelerometers.

High-sensitivity uniaxial opto-mechanical accelerometers provide very accurate linear acceleration measurements. In addition, an array of at least six accelerometers allows the estimation of linear and angular accelerations and becomes a gyro-free inertial navigation system. In this paper, we analyze the performance of such systems considering opto-mechanical accelerometers with different sensitivities and bandwidths. In the six-accelerometer configuration adopted here, the angular acceleration is estimated using a linear combination of accelerometers' read-outs. The linear acceleration is estimated similarly but requires a correcting term that includes angular velocities. Accelerometers' colored noise from experimental data is used to derive, analytically and through simulations, the performance of the inertial sensor. Results for six accelerometers, separated by 0.5 m in a cube configuration show noise levels of 10-7 m s-2 and 10-5 m s-2 (in Allan deviation) for time scales of one second for the low-frequency (Hz) and high-frequency (kHz) opto-mechanical accelerometers, respectively. The Allan deviation for the angular velocity at one second is 10-5 rad s-1 and 5×10-4 rad s-1. Compared to other technologies such as MEMS-based inertial sensors and optical gyroscopes, the high-frequency opto-mechanical accelerometer exhibits better performance than tactical-grade MEMS for time scales shorter than 10 s. For angular velocity, it is only superior for time scales less than a few seconds. The linear acceleration of the low-frequency accelerometer outperforms the MEMS for time scales up to 300 s and for angular velocity only for a few seconds. Fiber optical gyroscopes are orders of magnitude better than the high- and low-frequency accelerometers in gyro-free configurations. However, when considering the theoretical thermal noise limit of the low-frequency opto-mechanical accelerometer, 5×10-11 m s-2, linear acceleration noise is orders of magnitude lower than MEMS navigation systems. Angular velocity precision is around 10-10 rad s-1 at one second and 5×10-7 rad s-1 at one hour, which is comparable to fiber optical gyroscopes. While experimental validation is yet not available, the results shown here indicate the potential of opto-mechanical accelerometers as gyro-free inertial navigation sensors, provided the fundamental noise limit of the accelerometer is reached, and technical limitations such as misalignments and initial conditions errors are well controlled.

Angular velocity estimation of rotating plate using extended Kalman filter with accelerometer bias model

There has been researches on the gyro-free inertial navigation system (GF-INS), in which angular velocities are obtained from array of accelerometers instead of gyroscopes. The GF-INS can be effectively used for vehicles with high rotation rate when gyroscopes cannot provide the angular velocities due to their operating limitations. In order to obtain angular velocity, the measured accelerometer outputs are integrated and/or static model based estimators are used. When the integration is used, estimation performance of the angular velocity may be severely degraded due to the accelerometer error and depends on the initial velocity error. There is sign ambiguity problem of the angular velocity when the static model based estimator is used. In this paper, an angular velocity estimation algorithm of a rotating plate from centripetal acceleration and tangential acceleration measurements is proposed using an extended Kalman filter (EKF) with a random bias error model of the accelerometer. By using the EKF, effect of accelerometer error and initial velocity error can be reduced and sign ambiguity problem can be resolved. In order to show the validity of the proposed algorithm, an experimental setup is constructed. The angular velocity is estimated from outputs of a Micro-Electro Mechanical System accelerometer triad placed on the rotating plate. It can be seen from the results that performance of estimates by the EKF is better than those of other methods.

Improving measurement quality of a MEMS-based gyro-free inertial navigation system

This paper introduces a solution for increasing the measurement quality of the Micro Electro Mechanical System (MEMS) accelerometers in practical applications. This solution consists of two divisions. The first division is a solid carrier with five planes on each of which, a 3-channel MEMS accelerometer is installed. The second division of the proposed solution is a novel but simple-structured software designed for achieving two crucial targets: removing spikes and noise from the output of the accelerometers without having any a priori information about the desired frequency bands. The second mission of the processing software is to participate all accelerometers according to their measurement quality for determining the solid case accelerations. The criterion for ranking the channels of the accelerometers is based on their correlation index with the most informative channels among all operating accelerometers. By some simulation studies, the accuracy of the proposed solution for correct identification of spikes is about 99.99%. Also, by adding randomly the random-walk noise and spikes to the channels of the embedded accelerometers and then by altering the amount of input noise, the relative error of the rigid body acceleration is obtained versus input acceleration quality. As an instance, by implementing the proposed noise and spike reduction algorithm to a measurement signal with the quality value about 0.90, the relative navigation error-rate is enhanced from 0.75s−1 to 0.1s−1 for linear positions and 1s−1 to 0.12s−1 for angular attitudes. Also, the full-scale error-rates associated with the aforementioned relative-error rates are 0.28s−1, 0.02s−1, 0.2s−1, 0.01s−1, respectively.

Gyro-Free INS Theory

A Gyro-Free Inertial Measurement Unit (GF-IMU) uses a configuration of accelerometers only to measure the linear and angular acceleration of a rigid body in 3-D space, wherefrom the mechanization equations are integrated to yield the navigation state. Whereas in the past attempts were made to design microelectromechanical systems (MEMS) based GF-IMUs, the renewed interest in GF-IMUs springs from the possibility offered by cold atom interferometry-based high-accuracy acceleration measurements. Thus, in this paper, the Gyro-Free Inertial Navigation System (GF-INS) theory is developed. The mechanization equations for a gyro-free inertial navigation system are obtained, and the GF-IMU navigation state's error equations are derived. The latter afford the prediction of the navigation accuracy of a GF-INS using covariance analysis, and also the development of a Kalman filter for optimally fusing the GF-INS measurements with the measurements provided by additional sensors, for example, a ring laser gyro or navigation quality conventional INS. Copyright © 2013 Institute of Navigation.

Gyroscope Free 관성 항법 장치의 데이터 보정을 위한 퍼지 추론 시스템

This paper presents a study on the calibration of accelerometer data in the gyroscope free inertial navigation system(GFINS) using fuzzy inference system(FIS). The conventional INS(inertial navigation system) which can measure yaw rate and linear velocity using inertial sensors as the gyroscope and accelerometer. However, the INS is difficult to design as small size and low power because it uses the gyroscope. To solve the problem, the GFINS which does not have the gyroscope have been studied actively. However, the GFINS has cumulative error problem still. Hence, this paper proposes Fuzzy-GFINS which can calibrate the data of an accelerometer using FIS consists of two inputs that are ratio between linear velocity of the autonomous ground vehicle(AGV) and the accelerometer and ratio between linear velocity of the encoders and the accelerometer. To evaluate the proposed Fuzzy-GFINS, we made the AGV with Mecanum wheels and applied the proposed Fuzzy-GFINS. In experimental result, we verified that the proposed method can calibrate effectively data of the accelerometer in the GFINS.

Attitude Resolution Algorithm of Gyro-Free Inertial Navigation System

Gyro-Free Inertial Navigation System(GFINS) is constructed mainly with accelerometers. A nine-accelerometer GFINS scheme is designed and the prototype is manufactured. Then quaternion based attitude resolution algorithm is developed, the algorithm is also applied to the GFINS prototype, the results show that the algorithm can effectively offspring the cone-effect, and the attitude is resolute validly, the maximum attitude error is up to 0.37°, the maximum azimuth error is 0.47°.

GPS-based Land Vehicle Navigation System Assisted by a Low-Cost Gyro-Free INS Using Neural Network

GPS-based land vehicle navigation systems are subject to signal fading in urban areas and require aid from other enabling sensors. A low-cost gyro-free inertial navigation system (INS) without accumulated attitude errors and complicated initializations could be an effective solution to the problem. This paper investigates a Constrained Navigation Algorithm (CNA) and the Artificial Neural Network (ANN) technique to compensate velocity output from a gyro-free INS. The vehicle's heading will be calibrated by a full circle test so that the magnetometer's bias and scale factor error could be removed. Experiments with a vehicle driven over level terrain have been conducted to assess the performance of the compensated gyro-free INS solutions. The effect of the architecture of Neural Network on prediction performance has also been discussed as well as the applicability of the proposed solution to land vehicle navigation with GPS outages.

A platformles s inertial navigation system based on a canonical gravitational gradiometer

An algorithm is constructed for the ideal functioning of a platformless and gyroscope-free inertial navigation system which uses the readings from 18 accelerometers, which can be jointly considered as a gravitational gradiometer.