Initial Alignment Method Research Articles

Multi-Position Inertial Alignment Method for Underground Pipelines Using Data Backtracking Based on Single-Axis FOG/MIMU.

The inertial measurement method of pipelines utilizes a Micro-Electro-Mechanical Systems Inertial Measurement Unit (MIMU) to get the three-dimensional trajectory of underground pipelines. The initial attitude is significant for the inertial measurement method of pipelines. The traditional method to obtain the initial attitude uses three-axis magnetometers to measure the Earth's magnetic field. However, the magnetic field in urban underground pipelines is intricate, which leads to the initial attitude being inaccurate. To overcome this challenge, a novel multi-position initial alignment method based on data backtracking for a single-axis FOG and a three-axis Micro-Electro-Mechanical Inertial Measurement Unit (MIMU) is proposed. Firstly, the configuration of the sensors is determined. Secondly, according to the three-point support structure of the pipeline measuring instrument, a three-position alignment scheme is designed. Additionally, an initial alignment algorithm using the data backtracking method is developed. In this algorithm, a rough initial alignment is conducted by the data from single-axis FOG, and a fine initial alignment is conducted by the data from FOG/MIMU. Finally, an experiment was conducted to validate this method. The experiment results indicate that the pitch and roll angle errors are less than 0.05°, and the azimuth angle errors are less than 0.2°. This improved the precision of the 3-D trajectory of underground pipelines.

An Improved Initial Alignment Method Based on SE2(3)/EKF for SINS/GNSS Integrated Navigation System with Large Misalignment Angles.

This paper proposes an improved initial alignment method for a strap-down inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system with large misalignment angles. Its methodology is based on the three-dimensional special Euclidean group and extended Kalman filter (SE2(3)/EKF) and aims to overcome the challenges of achieving fast alignment under large misalignment angles using traditional methods. To accurately characterize the state errors of attitude, velocity, and position, these elements are constructed as elements of a Lie group. The nonlinear error on the Lie group can then be well quantified. Additionally, a group vector mixed error model is developed, taking into account the zero bias errors of gyroscopes and accelerometers. Using this new error definition, a GNSS-assisted SINS dynamic initial alignment algorithm is derived, which is based on the invariance of velocity and position measurements. Simulation experiments demonstrate that the alignment method based on SE2(3)/EKF can achieve a higher accuracy in various scenarios with large misalignment angles, while the attitude error can be rapidly reduced to a lower level.

Robust Adaptive SINS/DVL Initial Alignment Method Based on Variational Bayesian Information Filter

Robust Adaptive SINS/DVL Initial Alignment Method Based on Variational Bayesian Information Filter

SLAM-RAMU: 3D LiDAR-IMU lifelong SLAM with relocalization and autonomous map updating for accurate and reliable navigation

PurposeAutonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.Design/methodology/approachThis research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.FindingsThe performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.Originality/valueCompared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.

A Robust FOAM Initial Alignment Method for SINS Assisted by DVL

A Robust FOAM Initial Alignment Method for SINS Assisted by DVL

A Unified Initial Alignment Method of SINS Based on FGO

A Unified Initial Alignment Method of SINS Based on FGO

Dual-antenna GNSS-aided robust MEMS IMU initial alignment under the earth-centered-earth-fixed frame

The micro-electro-mechanical system (MEMS), with its small size, low cost and low power, has attracted widespread attentions in recent year, and the initial alignment of the inertial measurement unit (IMU) is an indispensable step before the navigation computation. In the traditional alignment methods with only the velocity measurements, the yaw angle is subject to divergence under low dynamic scenarios, and the measurements are also susceptible to the external environments. Furthermore, the navigation frame (n frame) is usually chosen as the reference frame. Although intuitive, it is more computationally complex compared with the earth-centered-earth-fixed frame (e frame). Therefore, to improve the alignment performance and computational efficiency, a robust MEMS IMU initial alignment method with yaw assistance under the e frame is proposed. In this method, to address the issues of the yaw divergency and computational inefficiency in the traditional methods, the relationship between the yaw angle, derived from dual-antenna global navigation satellite system baseline solution, and IMU error is derived and incorporated into the state space model with e frame as the reference frame. Besides, a robust extended Kalman filtering is adopted to further improve the system’s performance of outlier resistance, whose robust factors are determined by the Institute of Geodesy and Geophysics Ⅲ model. To validate the performance of the proposed method, two field experiments with the velocity of about 0.7 m s−1 and 1.5 m s−1 was conducted, and the collected data were processed for contrastive analysis. The results show that the effectiveness of the proposed method at suppressing yaw angle divergence and improving the alignment accuracy by resisting outliers under low dynamic scenarios compared with the traditional method. Moreover, the elapsed time is reduced by about 11% when the reference frame in data processing is switched from the n frame to the e frame.

Performance evaluation of coarse alignment methods for autonomous underwater vehicles in mooring conditions

The coarse alignment stage of inertial navigation systems (INS) is a step of paramount importance, specially for vehicles that employ the latter as a reference navigation solution, as is the case of autonomous underwater vehicles (AUVs). For such class of vehicles, the alignment has encumbrances that goes beyond those usually reported for terrestrial and aerial systems, as AUVs generally align at non benign environments (e.g., under swaying and mooring conditions) and have limited auxiliary navigation sensors. This work, hence, proposes a comparative analysis of the most representative coarse alignment methods applied to AUVs in mooring condition. The alignment approach herein explored, throughout its optimized and non-optimized versions, is the attitude decomposition-based initial alignment (ADIA) method, and the proposed comparison procedure tests different types of observation vectors resolved in the frames of interest. Experimental tests are conducted, which are based on kinematic data collected from an unmanned marine vehicle representing the typical swaying and attitude oscillations of AUVs in the mooring state, i.e., when subject to wave induced disturbances. Results lead to practical conclusions that can be applied to the AUV initial attitude estimation and are related to autonomy, accuracy and precision of the methods, as well as to their convergence rate and computational effort.

In-motion initial alignment method using Cayley–Kalman filter on special orthogonal group

Aiming at the in-motion initial alignment problem of strapdown inertial navigation system (SINS), this paper proposed a precise direct in-motion alignment method based on attitude matrix representation and Cayley–Kalman filter on the special orthogonal group. There are two innovations in this paper. First, as an element of the special orthogonal group [Formula: see text], the error attitude matrix is used to construct an in-motion alignment model, and it is transformed into the vector space to represent the attitude error state. The error matrix was used as a state to decrease the influence of the state-dependent problem. Second, this method uses the Cayley transform to replace the Taylor expansion to derive the update equation, which is a no approximation mapping relation to assure accuracy when deriving the filtering algorithm. Simulation and experimental results demonstrate the proposed alignment method has advantages over the existing methods in alignment accuracy and alignment time and performs well in the in-motion alignment process of SINS.

Effect of various printing parameters on the accuracy (trueness and precision) of 3D-printed partial denture framework

ObjectivesTo measure and compare the accuracy of 3D-printed materials used for RPD production to improve workflow and eliminate errors in manufacturing. MethodsA partially edentulous maxilla (Kennedy Class III, modification 1) was prepared and designed with proximal plates, rest seats and clasps in one first premolar, one canine and two second molars. A total of 540 3D printed RPD frameworks were 3D printed with three different types of resin (DentaCAST (Asiga, Australia), SuperCAST (Asiga, Australia) and NextDent (3D Systems, Netherlands)). To evaluate the trueness of the printing materials, they were printed with three types of layer thickness: 50 μm, 75 μm and 100 μm, using two types of build angles: 0° and 45° and three types of plate locations: side, middle and corner. After production, all specimens were scanned and superimposed with a control sample that was digitally designed. Using the initial alignment and best-fit alignment method, the root mean square error (RMSE) was calculated. To capture region specific discrepancy, 10 points of XYZ internal discrepancy within RPDs were measured and Euclidean error was calculated. Data was statistically analysed using Shapiro-Wilk and Kruskal-Wallis tests, one-way ANOVA and T-test (SPSS Version 29) and MATLAB (R2022b). ResultsOptimal results were found using 45°, middle of the build plate and layer thicknesses of 100 μm (115 ± 19 μm, DentaCAST), 75 μm (143 ± 14 μm, NextDent), 50 μm (98 ± 35 μm, SuperCAST), which were clinically acceptable. Results were statistically significant when comparing layer thickness in each testing group (p < 0.001). Layer thickness was a primary parameter in the determination of print accuracy among all materials (p < 0.001). Higher discrepancies and failures were observed in 0° prints. No statistically significant difference was found in material usage between build angles or layer thickness (p > 0.005). ConclusionsAll three 3D printing materials exhibited clinically acceptable RMSE results with a build angle of 45° with a printing layer thickness of 50 μm for SuperCAST, 75 μm NextDent and 100 μm for DentaCAST. The highest discrepancies were mostly found in posterior clasps, while the lowest discrepancy was found in palatal straps. Despite unoptimized spacing of prints, frameworks configured to print in the middle of the build plate result in the least printing failures.

Two-position initial alignment method for redundant rotating inertial navigation system

Two-position initial alignment method for redundant rotating inertial navigation system

경사 발사 유도무기체계에서 함상 저장 전달정렬 가능성 연구

Shipboard transfer alignment is a procedure for determining the initial navigation information of the ship-mounted guided weapon system’s SINS by receiving the surface ship INS information. In order to improve the navigation accuracy of the ship-launched guided missile system, it is necessary to enhance the precision and accuracy of the shipboard transfer alignment. Generally, the shipboard transfer alignment uses both velocity matching and attitude matching to increase observability. Estimating the misalignment error between SINS and MINS using an extended Kalman filter is necessary to obtain the initial attitude of SINS in shipboard transfer alignment to which attitude matching is applied. Because it takes several minutes for the estimated misalignment error to converge, it is essential to reduce the alignment time, especially in the case of weapon systems. This paper presents the possibility of utilizing the shipboard stored transfer alignment for navigation-grade INS mounted on the inclined launching system. There have been no reports of previous studies on the shipboard stored transfer alignment. In a similar research case, a rapid initial alignment method using the pre-performed alignment results was proposed to reduce the alignment time of tactical-grade INS, which was mounted on the launch platform with MINS. To verify the possibility of the shipboard stored transfer alignment, the existing shipboard transfer alignment was repeatedly performed using an inclined guided missile system deployed in the military. The results show that the repeatability and accuracy of the misalignment error satisfy the initial attitude error requirement. Therefore, based on the test results, it is verified that the shipboard stored transfer alignment for the inclined launching guided missile equipped with a navigation-grade INS is possible.

The development and evaluation of a combined initial alignment algorithm for strapdown inertial navigation system

This paper proposes a combined initial alignment algorithm for strapdown inertial navigation system, in which the initial alignment is carried out in two stages during the process of preflight preparations while the aircraft is not moving. This method of initial alignment has the advantages of being conducted based on the Earth’s equatorial (Greenwich) coordinate system, which is also used by the aircraft onboard navigation system. In the first stage, the coarse initial alignment is carried out, and this is done according to the orientation parameters using the vector matching method. Then, in the second stage, a fine procedure is carried out, which may be looked at as an integrated system implemented using optimal Kalman filter. In this case, the initial parameters of orientation and navigation are refined, as well as the systematic errors of the inertial sensors are estimated and corrected. The simulation results show that the required level of initial alignment accuracy could be achieved by applying coarse and fine initial alignment procedures not sequentially but in a combination.

A high-accuracy initial alignment method based on backtracking process for strapdown inertial navigation system

When the alignment time is short, the traditional initial alignment method of strapdown inertial navigation system has very low accuracy, and even the attitude angle error is difficult to converge. We propose a high-accuracy initial alignment method for strapdown inertial navigation system based on the backtracking process, which reuses short-time measurement data to improve the accuracy and speed of initial alignment. We introduce the backtracking process into coarse alignment and fine alignment of strapdown inertial navigation system. In the coarse alignment, the instantaneous acceleration error caused by the backtracking process is estimated and compensated. In the fine alignment, the optimal initialization method of the parameters in the Kalman filter is given. The initial alignment experimental results of strapdown inertial navigation system prove the effectiveness of the proposed method. Compared with the previous initial alignment method, the accuracy and speed of the proposed method are improved.

A Vision Aided Initial Alignment Method of Strapdown Inertial Navigation Systems in Polar Regions.

The failure of the traditional initial alignment algorithm for the strapdown inertial navigation system (SINS) in high latitude is a significant challenge due to the rapid convergence of polar longitude. This paper presents a novel vision aided initial alignment method for the SINS of autonomous underwater vehicles (AUV) in polar regions. In this paper, we redesign the initial alignment model by combining inertial navigation mechanization equations in a transverse coordinate system (TCS) and visual measurement information obtained from a camera fixed on the vehicle. The observability of the proposed method is analyzed under different swing models, while the extended Kalman filter is chosen as an information fusion algorithm. Simulation results show that: the proposed method can improve the accuracy of the initial alignment for SINS in polar regions, and the deviation angle has a similar estimation accuracy in the case of uniaxial, biaxial, and triaxial swing modes, which is consistent with the results of the observable analysis.

AN INITIAL ALIGNMENT METHOD OF INERTIAL NAVIGATION SYSTEM FOR THE STATIC STATE

Abstract. The navigation means the process of determining the position, velocity, and orientation of the moving object such as the land vehicle, aerial vehicle, and even autonomous vehicle. Nowadays, Global Navigation Satellite System (GNSS) is most used for positioning. Nevertheless, the navigation system based on GNSS would be interrupted in the challenging environments. Therefore, the inertial navigation system (INS) has been widely combined with GNSS to overcome this issue. For INS, the core concept is the measurements of Inertial Measurement Unit (IMU). In order to integrate the measurements of IMU with several sensors such as GNSS receivers, odometers, and so on, we should transform the measurements of IMU from body frame to navigation frame. The initial alignment just represents the process of finding the accurate initial rotation matrix between body frame and navigation frame in the beginning of navigation. However, initial misalignment angles would cause large error of INS. Hence, obtaining an accurate initial rotation matrix from body frame to navigation frame is an important issue to get the better navigation performance. In the research, an integrated navigation system is developed to validate the initial alignment algorithm. With the data pre-processing and accurate calibration. the proposed method of initial alignment can get precise initial ration matrix. The errors of roll pitch, and yaw angle are all smaller than 1 degree after initial alignment. Moreover, the time threshold of initial alignment is set by manual traditionally. A method of finding threshold of coarse alignment is proposed in this research.

An improved initial alignment method based on adaptive robust CKF algorithm

When the misalignment angle is a large angle in the strapdown inertial navigation system (SINS), it is necessary to establish a nonlinear error model to estimate the error. Hence, an improved initial alignment method based on adaptive robust CKF algorithm is proposed in this paper. Firstly, based on the analysis results, SINS/GPS nonlinear error model is established. Secondly, in the view of observation gross errors and inaccurate noise statistical characteristics, adaptive robust CKF algorithm is designed. Finally, according to simulation analysis and experiment, adaptive robust CKF algorithm can augment the stability, improve filter estimation accuracy and convergence rate, which significantly improves the initial alignment ability of strapdown inertial navigation system at large azimuth misalignment angle.

Carrier-Phase-Based Initial Heading Alignment for Land Vehicular MEMS GNSS/INS Navigation System

For a micro-electromechanical system (MEMS)-based inertial navigation system (INS) and global navigation satellite system (GNSS) integrated system in land vehicular applications, rapid and accurate initial heading alignment is still a challenge. We propose an initial heading alignment method for MEMS INS using the GNSS carrier-phase measurement, based on the basic principle of trajectory similarity. The proposed method performs “trajectory matching” in the line-of-sight direction of satellites, where the angle is obtained by comparing the actual observed time-differenced carrier phase (TDCP) and INS-derived TDCP. Experimental results show that the initial heading could be determined accurate to 0.65° and 1.68° at a 95% confidence level within 5 s under open sky conditions and in typical urban environments, respectively, using a typical MEMS inertial measurement unit (e.g., STIM300, Safran Sensing Technologies Norway) and high-quality GNSS receiver (NovAtel OEM6). The proposed method is also verified using low-cost GNSS receivers and low-cost IMU chip under different environment. The proposed method performs better than existing approaches in terms of both time efficiency and accuracy.

In-Motion Initial Alignment Method Based on Vector Observation and Truncated Vectorized K-Matrix for SINS

In this paper, an improved in-motion coarse alignment method is proposed for the strapdown inertial navigation system (SINS) aided by the global positioning system (GPS). Traditional in-motion alignment methods suffer from complex noises contained in the outputs of inertial sensors and GPS. To solve this problem, this paper proposes an in-motion coarse alignment method using the vector observation and truncated vectorized <i>K</i>-matrix (VO-TVK) for autonomous underwater vehicles. The contributions of this study are twofold. Firstly, a new simplified model can be applied to the in-motion alignment process by employing the zero-trace and symmetry of the <i>K</i>-matrix. Secondly, the proposed VO-TVK algorithm can make up for the Optimal-REQUEST algorithm&#x2019;s drawbacks, where the Optimal-REQUEST algorithm has the conservative covariance matrix and the scalar gain. The simulation, vehicle test and lake trial results illustrate that the proposed VO-TVK algorithm can efficiently reduce the effects of noises contained in the vector observation and achieve better accuracy than the compared algorithms.

In-motion initial alignment method for a laser Doppler velocimeter-aided strapdown inertial navigation system based on an adaptive unscented quaternion H-infinite filter

With its advantages of high velocity measurement accuracy and fast dynamic response, the laser Doppler velocimeter (LDVs) is expected to replace the odometer (OD) in combination with a strapdown inertial navigation system (SINS) to give a higher-precision integrated navigation system. Since a LDV has higher velocity measurement accuracy and data update frequency than an OD and Doppler velocity log, a LDV is used for the first time in this paper to aid a SINS in in-motion alignment. Considering that some approximation is used in the alignment model, the uncertainty noise of the sensors during the motion process and the unknown noise parameters during the filter process, an adaptive unscented quaternion H-infinite estimator (AUSQUHE) is proposed. The proposed AUSQUHE method has high robustness since it combines the advantages of an unscented quaternion estimator and H-infinite filter. The adaptive threshold of the H-infinite filter and the adaptive measurement noise covariance matrix are introduced to make the filter adapt to the changing environment and accelerate the convergence of errors. The performance of the proposed method is verified by a vehicle field test with a normal LDV signal and a vehicle test with the LDV signal disturbed by noise. The results show that the proposed method has higher alignment accuracy, faster convergence speed and stronger robustness than the four other compared methods.