Accuracy Of Inertial Measurement Units Research Articles

Permutation fuzzy entropy based ICEEMDAN de-noising for inertial sensors

As one of the critical factors affecting the accuracy of inertial navigation system (INS) positioning, random noise will lead to the accumulation of INS positioning errors, even the failure of the system. Previous studies mainly focus on achieving noise reduction by adopting either the wavelet decomposition method or some relevant methods of the empirical mode decomposition (EMD). However, the former is limited by Heisenberg’s uncertainty principle when processing non-stationary signals, and the latter exhibits numerous mode-mixing phenomena during the decomposition process. In this paper, a permutation fuzzy entropy (PFE) based improved complete ensemble EMD with adaptive noise (ICEEMDAN) de-noising method is proposed to utilize the ICEEMDAN method for signal decomposition, and the PFE as an index to distinguish the effective intrinsic mode functions (IMFs) and the noisy IMFs during the decomposition process. In addition, simulated signals with different noise levels and field data with different durations are utilized to design a group of experiments to test the de-noising performance of the PFE-based ICEEMDAN de-noising method. The experimental results demonstrate that the advantages of our proposed method consists of three main parts: (1) The ICEEMDAN method more effectively alleviates the mode-mixing problem compared to the complete ensemble EMD with adaptive noise (CEEMDAN), thereby enhancing the accuracy of inertial measurement unit (IMU) signal reconstruction; (2) the PFE is more preferable than the permutation entropy for classify IMFs decomposed by the ICEEMDAN method; (3) our proposed method can effectively reduce IMU signal noise and improve the positioning accuracy of inertial sensor.

An Improved Inertial Preintegration Model in Factor Graph Optimization for High Accuracy Positioning of Intelligent Vehicles

Accurate and reliable positioning is of great importance for the realization of intelligent vehicles (IV). Factor graph optimization (FGO) has been popularized in the field of robotics for state estimation. As a ubiquitous sensor, the IMU is widely used for vehicular positioning based on the preintegration theory using FGO. However, the existing preintegration model fails to consider the earth's rotation, which challenges the attitude and positioning performance of vehicles equipped with high-precision inertial measurement units (IMU). To fill this gap, an accurate IMU factor that accounts for the earth's rotation rate is designed in this paper. First, an exact preintegration measurement model is derived based on a high-precision inertial navigation system (INS) mechanization. The proposed factor is more accurate and it enables the application of the FGO more suitable for inertial-based integrated navigation with different precision IMUs. In both simulation and field tests, the INS/ Global Navigation Satellite System (GNSS) integration, as a representative of inertial-based integrated navigation, is used to verify the performance of the proposed preintegration factor. The navigation results using our experimental car reveal that the proposed model leads to a more accurate estimation, outperforming the traditional preintegration models, especially in the aspect of attitude estimation.

Can inertial measurement unit sensors evaluate foot kinematics in drop foot patients using functional electrical stimulation?

The accuracy of inertial measurement units (IMUs) in measuring foot motion in the sagittal plane has been previously compared to motion capture systems for healthy and impaired participants. Studies analyzing the accuracy of IMUs in measuring foot motion in the frontal plane are lacking. Drop foot patients use functional electrical stimulation (FES) to improve walking and reduce the risk of tripping and falling by improving foot dorsiflexion and inversion-eversion. Therefore, this study aims to evaluate if IMUs can estimate foot angles in the frontal and sagittal planes to help understand the effects of FES on drop foot patients in clinical settings. Two Gait Up sensors were used to estimate foot dorsi-plantar flexion and inversion-eversion angles in 13 unimpaired participants and 9 participants affected by drop foot while walking 6 m in a straight line. Unimpaired participants were asked to walk normally at three self-selected speeds and to simulate drop foot. Impaired participants walked with and without FES assistance. Foot angles estimated by the IMUs were compared with those measured from a motion capture system using curve RMSE and Bland Altman limits of agreement. Between participant groups, overall errors of 7.95° ± 3.98°, -1.12° ± 4.20°, and 1.38° ± 5.05° were obtained for the dorsi-plantar flexion range of motion, dorsi-plantar flexion at heel strike, and inversion-eversion at heel strike, respectively. The between-system comparison of their ability to detect dorsi-plantar flexion and inversion-eversion differences associated with FES use on drop foot patients provided limits of agreement too large for IMUs to be able to accurately detect the changes in foot kinematics following FES intervention. To the best of the authors' knowledge, this is the first study to evaluate IMU accuracy in the estimation of foot inversion-eversion and analyze the potential of using IMUs in clinical settings to assess gait for drop foot patients and evaluate the effects of FES. From the results, it can be concluded that IMUs do not currently represent an alternative to motion capture to evaluate foot kinematics in drop foot patients using FES.

Wheelchair Rugby Sprint Force-Velocity Modeling Using Inertial Measurement Units and Sport Specific Parameters: A Proof of Concept.

Para-sports such as wheelchair rugby have seen increased use of inertial measurement units (IMU) to measure wheelchair mobility. The accessibility and accuracy of IMUs have enabled the quantification of many wheelchair metrics and the ability to further advance analyses such as force-velocity (FV) profiling. However, the FV modeling approach has not been refined to include wheelchair specific parameters. The purpose of this study was to compare wheelchair rugby sprint FV profiles, developed from a wheel-mounted IMU, using current mono-exponential modeling techniques against a dynamic resistive force model with wheelchair specific resistance coefficients. Eighteen athletes from a national wheelchair rugby program performed 2 × 45 m all-out sprints on an indoor hardwood court surface. Velocity modelling displayed high agreeability, with an average RMSE of 0.235 ± 0.07 m/s-1 and r2 of 0.946 ± 0.02. Further, the wheelchair specific resistive force model resulted in greater force and power outcomes, better aligning with previously collected measures. The present study highlights the proof of concept that a wheel-mounted IMU combined with wheelchair-specific FV modelling provided estimates of force and power that better account for the resistive forces encountered by wheelchair rugby athletes.

Observability Analysis and Optimization of Cooperative Navigation System With a Low-Cost Inertial Sensor Array

Low-cost inertial measurement unit (IMU) is gradually applied for providing reliable positioning and navigation information in the area of Internet of Things (IoT) applications recently. However, the accuracy of IMU is highly influenced by inertial sensor errors in GNSS-denied and indoor navigation environment. In order to improve the accuracy and robustness of IMU, rotation modulation and cooperative navigation techniques can serve as effective ways for indoor and outdoor seamless navigation and positioning. In this paper, we propose a cooperative navigation system with a low-cost inertial sensor array composed of four IMUs. For optimizing the configuration and information processing of this system, observability analysis is carried out based on the concept of the degree of observability. Furthermore, a criterion for calculating the degree of observability is formulated to simplify the observability analysis of rotational IMUs. According to the simulation experiments of unmanned vehicle at low, medium and high driving speeds, the IMU rotation technique can improve the observability of inertial sensor errors and thus increase the accuracy of orientation, while the cooperative navigation technique can highly improve the accuracy of positioning.

VQF: Highly accurate IMU orientation estimation with bias estimation and magnetic disturbance rejection

The miniaturization of MEMS-based inertial measurement units (IMUs) facilitates their widespread use in a growing number of application domains. The fundamental sensor fusion task of orientation estimation is a prerequisite for most further data processing steps in inertial motion tracking, such as position and velocity estimation, joint angle estimation, and 3D visualization. Errors in the estimated orientations severely affect all further processing steps. Recent systematic comparisons of existing algorithms show that out-of-the-box accuracy is often low and that application-specific tuning is required to obtain high accuracy. In the present work, we propose and extensively evaluate a quaternion-based orientation estimation algorithm that is based on a novel approach of filtering the acceleration measurements in an almost-inertial frame and that includes extensions for gyroscope bias estimation and magnetic disturbance rejection, as well as a variant for offline data processing. In contrast to all existing work, we perform an extensive evaluation, using a large collection of publicly available datasets and eight literature methods for comparison. The proposed method consistently outperforms all eight literature methods and achieves an average RMSE of 2.9°, while the errors obtained with literature methods range from 5.3° to 16.7°. This improved accuracy with respect to the state of the art is observed not only in average but also for each of several different motion characteristics, as well as for gyroscope bias estimation. Since the evaluation was performed with one single fixed parametrization across a very diverse dataset collection, we conclude that the proposed method provides unprecedented out-of-the-box performance for a broad range of motions, sensor hardware, and environmental conditions. This gain in orientation estimation accuracy is expected to advance the field of IMU-based motion analysis and provide performance benefits in numerous applications. The provided open-source implementation makes it easy to employ the proposed method.

Accuracy of Inertial Measurement Units When Applied to the Countermovement Jump of Track and Field Athletes.

In this study, we aimed to assess the countermovement jump (CMJ) using a developed instrument encompassing an off-the-shelf Inertial Measurement Unit (IMU) in order to analyze performance during the contraction phase, as well as to determine the jump height and the modified reactive strength index (RSImod), using force plate (FP) data as reference. Eight athletes (six males and two females) performed CMJs with the IMU placed on their fifth lumbar vertebra. Accuracy was measured through mean error (standard deviation), correlation, and comparison tests. The results indicated high accuracy, high correlation (r), and no statistical differences between the IMU and the FP for contraction time (r = 0.902; ρ < 0.001), negative impulse phase time (r = 0.773; ρ < 0.001), flight time (r = 0.737; ρ < 0.001), jump time (r = 0.708; ρ < 0.001), RSImod (r = 0.725; ρ < 0.001), nor minimum force (r = 0.758; ρ < 0.001). However, the values related to the positive impulse phase did not have the expected accuracy, as we used different devices and positions. Our results demonstrated that our developed instrument could be utilized to identify the contraction phase, jump height, RSImod, and minimum force in the negative impulse phase with high accuracy, obtaining a signal similar to that of an FP. This information can help coaches and athletes with training monitoring and control, as the device has simpler applicability making it more systematic.

New Measurement Methods for Structure Deformation and Objects Exact Dimension Determination

The current rapid development of technologies enables new procedures for deformation and the detecting of construction defects and their modelling and monitoring in BIM. New instruments were developed for fast and sufficiently accurate mapping like personal mobile laser scanners (PLS). In the world of photography, the size of camera sensors is bigger, and the photographs are sharper. The rapid development of computer performance enables automatic and complex calculations, which lead to large sets of detailed 3D data and a high degree of automation. This influences photogrammetry and its methods. The results are more detailed and more accurate. Deformation, defects and exact dimensions (metrology) of different structures or objects can be currently measured by digital close-range photogrammetry. Cracks and cavities are monitored for structure status detection. This is important for planning reconstruction and for financial reasons. For structures like cooling towers, chimneys, or bridges can be created on a 3D model with a high texture resolution for finding and monitoring cracks and cavities. Deformations or defects that were found must be in scale, and measurable for the calculation of the scope of repair work and its price. The generated 3D object model can then be used for further measurements, for the price estimation of renovation, and for the creation of a BIM, in which all processes can be modelled and watched. Deformation can be monitored over time by creating additional models after a defined period. Captured 3D models from different periods can be compared in software like CloudCompare to determine the progress of degradational changes. The trend of the aging of the structure can be traced, which will be helpful for the reasonable planning of reconstruction. Based on the rapid development and miniaturization of measuring devices, new, smaller, easier to use, and more perfect devices are constructed. This also applies to the new group of laser scanners constructed for basic measurement and structure modeling for BIM. Conventional laser scanners can be accurate, but they are relatively large and heavy, difficult to transport and measuring with them is relatively slow (stop and go type). If the project goal is the classic construction, documentation of the object, data transfer to BIM or basic documentation of objects, PLS is the ideal device. Thanks to the development of accurate IMU (inertial measurement unit) and SLAM (simultaneous localization and mapping) technologies, these devices are on the rise. The forthcoming article will inform about the methods of accurate close-range photogrammetry and mobile laser scanning and will show their advantages with specific examples.

Evaluation of head posture using an inertial measurement unit

An inertial measurement unit (IMU) is an electronic device that measures and track the orientation of a body. We conducted this study in accordance with the STARD guidelines to evaluate the accuracy of IMU (index test) for measuring head posture compared to the current gold standard using a cervical range of motion (CROM) device. The reproducibility of the hunter and mirror-guided head posture was also evaluated. In vitro and in vivo tests were carried out to assess the validity of the IMU. To assess reproducibility, thirty healthy young adults were asked to look at four different locations in two different sessions while the head posture was recorded. Excellent correlation (r = 0.99; p < 0.001) was found between the IMU and CROM device with an absolute mean difference of 0.45° ± 0.58° (p = 0.85) for the in vitro test and 0.88° ± 1.20° (p = 0.99) for the in vivo test. For the reproducibility test, moderate to good correlation coefficients were found (r = 0.55 to 0.89; all p < 0.05) between the two sessions. The intraclass correlation coefficient ranged from moderate to excellent reliability (ICC from 0.74 to 0.96). These results suggest that the IMU sensors, when calibrated correctly, can be adequate to analyze head posture.

Machine Learning to Improve Orientation Estimation in Sports Situations Challenging for Inertial Sensor Use.

In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7° root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations.

Validity, reliability and accuracy of inertial measurement units (IMUs) to measure angles: application in swimming

ABSTRACT The first objective was to test the validity, reliability and accuracy of paired inertial measurement units (IMUs) to assess absolute angles relative to Vicon and OptiTrack systems. The potential impacts of slow vs. rapid and intermittent vs. continuous movements were tested during 2D laboratory analyses and 3D ecological context analysis. The second objective was to test the IMUs alone in an ecological activity (i.e., front crawl) that encompassed the previous independent variables to quantify inter-cyclic variability. Slow and intermittent motion ensured high to reasonable validity, reliability and accuracy. Rapid motion revealed an out-of-phase pattern for temporal reliability and lower validity, which was also visible in 3D. Also, spatial reliability and accuracy decreased in 3D, mainly due to discrepancies in local maximums, whereas temporal reliability remained in-phase. For the second objective, inter-cyclic variability did not exceed 12° based on root mean square error (RMSE). Therefore, IMUs should be considered valuable supplements to optoelectronic systems if users carefully position the sensors in rigid clusters and calibrate them to integrate potential offsets. Drift correction by spline interpolation or normalisation of the absolute data should also be considered as additional techniques that increase IMU performance in ecological contexts of performance.

Typical Errors, Accuracy Classes and Currently Expected Accuracy of Inertial Measurement Units

Inertial measurement units can nowadays be found in a wide range of applications, from consumer electronics to precision surveying systems requiring high accuracy. IMUs are also widely used in aviation, especially for UAVs. The gyroscopes and accelerometers in IMUs have specific operating modes that would result in specific errors typical of such instruments. This paper reviews the typical errors of IMUs and the accuracy that can be achieved with these instruments today in each application area.

Accurate IMU Factor Using Switched Linear Systems for VIO

Accurate motion estimation plays a crucial role in state estimation of an unmanned aerial vehicle (UAV). This is usually carried out by fusing the kinematics of an inertial measurement unit (IMU) with the video output of a camera. However, the accuracy of existing approaches is hindered by the discretization effect of the model even at a high IMU sampling rate. In order to improve the accuracy, we propose a new IMU motion integration model for the IMU kinematics in continuous time. The kinematics are modeled using a switched linear system. A closed-form discrete formulation is derived to compute the mean measurement, the covariance matrix, and the Jacobian matrix. Thus, it is more accurate and more efficient for online estimation of visual-inertial odometry (VIO), particularly when there is a high dynamic change in the agent's motion or the agent travels with high speed. The proposed IMU factor framework is evaluated using both real public datasets and indoor environment under different scenarios of motion capture. Our evaluation shows that the proposed framework outperforms the state-of-the-art VIO approach by up to 22.71% accuracy improvement on the EuRoc dataset and 38.15% accuracy improvement for motion estimation under the indoor environment.

Development the method of pipeline bending strain measurement based on microelectromechanical systems inertial measurement unit.

With the development of pipeline construction, the additional stress and strain becomes the key factor to induce the damage for oil and gas pipeline. The in-line inspection of pipeline bending strain which is based on high-end tactical-grade inertial measurement unit has become routine practice for the oil and gas pipelines over recent years. However, these accurate inertial measurement units are large size and high cost limit to use in small diameter pipelines of bending strain inspection. Microelectromechanical systems-based inertial navigation has been applied to mapping the centerline of the small size pipeline, and the accurate trajectory and attitude information become key factors to calculate the bending strain of pipelines. This article proposed a method not only to calculate the pipeline bending strain but also to improve the accuracy for the bending strain based on the wavelet analysis. Tests show that this method can be effectively used in the calculation and optimization of the bending strain, and it will increase the accuracy to within 19.1% of the actual bending strain.

Validation of inertial measurement units with optical tracking system in patients operated with Total hip arthroplasty

BackgroundPatient reported outcome measurement (PROMs) will not capture in detail the functional joint motion before and after total hip arthroplasty (THA). Therefore, methods more specifically aimed to analyse joint movements may be of interest. An analysis method that addresses these issues should be readily accessible and easy to use especially if applied to large groups of patients, who you want to study both before and after a surgical intervention such as THA. Our aim was to evaluate the accuracy of inertial measurement units (IMU) by comparison with an optical tracking system (OTS) to record pelvic tilt, hip and knee flexion in patients who had undergone THA.Methods49 subjects, 25 males 24 females, mean age of 73 years (range 51–80) with THA participated. All patients were measured with a portable IMU system, with sensors attached lateral to the pelvis, the thigh and the lower leg. For validation, a 12-camera motion capture system was used to determine the positions of 15 skin markers (Oqus 4, Qualisys AB, Sweden). Comparison of sagittal pelvic rotations, and hip and knee flexion-extension motions measured with the two systems was performed. The mean values of the IMU’s on the left and right sides were compared with OTS data.ResultsThe comparison between the two gait analysis methods showed no significant difference for mean pelvic tilt range (4.9–5.4 degrees) or mean knee flexion range (54.4–55.1 degrees) on either side (p > 0.7). The IMU system did however record slightly less hip flexion on both sides (36.7–37.7 degrees for the OTS compared to 34.0–34.4 degrees for the IMU, p < 0.001).ConclusionsWe found that inertial measurement units can produce valid kinematic data of pelvis- and knee flexion-extension range. Slightly less hip flexion was however recorded with the inertial measurement units which may be due to the difference in the modelling of the pelvis, soft tissue artefacts, and malalignment between the two methods or misplacement of the inertial measurement units.Trial registrationThe study has ethical approval from the ethical committee “Regionala etikprövningsnämnden i Göteborg” (Dnr: 611–15, 2015-08-27) and all study participants have submitted written approval for participation in the study.

High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction

The efficient use of nitrogen fertilizer is a crucial problem in modern agriculture. Fertilization has to be minimized to reduce environmental impacts but done so optimally without negatively affecting yield. In June 2017, a controlled experiment with eight different nitrogen treatments was applied to winter wheat plants and investigated with the UAV-based hyperspectral pushbroom camera Resonon Pika-L (400–1000 nm). The system, in combination with an accurate inertial measurement unit (IMU) and precise gimbal, was very stable and capable of acquiring hyperspectral imagery of high spectral and spatial quality. Additionally, in situ measurements of 48 samples (leaf area index (LAI), chlorophyll (CHL), and reflectance spectra) were taken in the field, which were equally distributed across the different nitrogen treatments. These measurements were used to predict grain yield, since the parameter itself had no direct effect on the spectral reflection of plants. Therefore, we present an indirect approach based on LAI and chlorophyll estimations from the acquired hyperspectral image data using partial least-squares regression (PLSR). The resulting models showed a reliable predictability for these parameters (R2LAI = 0.79, RMSELAI [m2m−2] = 0.18, R2CHL = 0.77, RMSECHL [µg cm−2] = 7.02). The LAI and CHL predictions were used afterwards to calibrate a multiple linear regression model to estimate grain yield (R2yield = 0.88, RMSEyield [dt ha−1] = 4.18). With this model, a pixel-wise prediction of the hyperspectral image was performed. The resulting yield estimates were validated and opposed to the different nitrogen treatments, which revealed that, above a certain amount of applied nitrogen, further fertilization does not necessarily lead to larger yield.

Hip and Trunk Kinematics Estimation in Gait Through Kalman Filter Using IMU Data at the Ankle

The purpose of this paper is to provide a new method of estimating the hip acceleration and trunk posture in the sagittal plane during a walking task using an extended Kalman filter (EKF) and an unscented Kalman filter (UKF). A comparison between these two estimation techniques is also provided. Considering the periodic nature of gait, a modified biomechanical model with Fourier series approximations are utilized as a priori knowledge. Inertial measurement units (IMUs) are placed on the right side of the ankle, hip, and middle of the trunk of twenty recruited participants, as input, a posteriori data, and the ground truth for the model, separately. The results show a better performance of the EKF in estimating the hip acceleration (6.5% error) and the trunk posture (3.12% error). Moreover, both the EKF and the UKF provide low error rates for the trunk posture in comparison to the hip acceleration. This paper provides an inexpensive and novel method to estimate and filter the kinematics of motion for different body locations from a single accurate IMU attached to the ankle considering the periodic nature of gait that can be extended to other activities as well as real-time applications.

Effects of Movement Speed and Magnetic Disturbance on the Accuracy of Inertial Measurement Units.

This study evaluated the effects of motion speed and magnetic disturbance on the spatial orientation accuracy of an inertial measurement unit (IMU) on the hand. Thirteen participants performed six trials of a repetitive material transfer task. Movement speed (15, 30, 45 transfers/minute) and magnetic disturbance (absent, present) were the independent variables. Optical motion capture was the reference. Root-mean-square differences (RMSD) exceeded 20° when inclination measurements (pitch and roll) were calculated using the IMU accelerometer. A linear Kalman filter and a proprietary, embedded Kalman filter reduced inclination RMSD to <3° across all movement speeds. The RMSD in the heading direction (i.e., about gravity) increased (from <5° to 17°) under magnetic disturbance. The linear Kalman filter and the embedded Kalman filter reduced heading RMSD to <12° and <7°, respectively. Use of IMUs and Kalman filters can improve inclinometer measurement accuracy. However, magnetic disturbances continue to limit the accuracy of three-dimensional IMU motion capture.

The validation and application of Inertial Measurement Units to springboard diving

Inertial Measurement Units (IMUs) may offer an ecologically valid, reliable, and practical method for biomechanical performance analysis. With such potential in mind, Part 1 of this study examined the accuracy of IMUs gyroscopes with an optical system (Cortex 3.3). A calibration formula standardised the IMUs angular velocity output with the optical system. The percentage differences between the two measures = 0.5% (p < 0.05), suggest IMU’s efficacy for application. In Part 2, the aim was to examine and understand how dive flight angular velocity time series plots change and increase according to dive degree of difficulty. With IMUs attached to three competitive divers performing forward somersault dives, dive flight kinematics were assessed. Biomechanically, a 4½ tuck somersault dive differed to lower degree of difficulty dives in terms of: (1) a rotational delay immediately after takeoff (to gain greater vertical translation); (2) increased total time of flight; (3) greater muscle effort to resist increased centrifugal forces produced by the increased angular velocity (1,090 °/s); and (4) greater eccentric control during deceleration allow a safe and vertical entry into the water. IMUs can be effectively utilised and integrated into contexts such as springboard diving for performance analysis and optimisation purposes.

GPS-INS Integration Algorithm for Autonomous Vehicle Using Loosely Coupled Integration Architecture

Inertial measurement unit (IMU) has been widely used for autonomous vehicles navigation. The accuracy of IMU specifies the performance of the inertial navigation system (INS).The errors in the INS are mainly due to the IMU inaccuracies, initial alignment, computational errors and approximations in the system equations. These errors are further integrated over time due to the dead-reckoning nature of the INS, which leads to unacceptable results. These errors need an accurate estimation for high precision navigation. INS is integrated with Global Positioning System (GPS) to estimate the errors and enhance the navigation capability of the INS. Linearized Kalman Filter (LKF) is proposed for estimating the errors in the low cost INS using Loosely Coupled integration approach, which is opted for its simplicity and robustness. Prediction part of the LKF is used during the GPS lag for errors estimation, which is found very effective for low cost sensors. The resulting GPS-INS integration algorithm is evaluated on simulated Autonomous vehicle trajectory, generated from 6-DOF model. The integrated system limits the attitude errors less than 0.1 deg and velocity errors of the order of 0.003 meter per second. Furthermore, it provides an optimal navigation solution than can be achieved from individual systems.