Coarse Alignment Research Articles

Research on the Application of 3D Animation Technology in the Digital Reproduction and Brand Image Construction of Guibei Ancient Village Cultural Heritage

Abstract The inheritance and protection of the historical, cultural and folk art values of ancient villages in development are generally carried out in three aspects, namely, architectural elements, landscape elements and living elements. This paper applies a 3D animation scanner to collect the point cloud data of Guibei ancient villages and completes the pre-processing of the point cloud data by using the algorithm of SAC-IA coarse alignment + ICP fine alignment for the alignment. After that, the Poisson equation is optimized by using Gaussian filtering to realize the point cloud surface reconstruction of Guibei village buildings, and the cultural information texture extracted by the K-Means clustering algorithm is mapped to realize the digital reproduction of the cultural heritage of ancient villages in the Guibei. On this basis, the cultural brand image of Guibei ancient villages is constructed from multiple perspectives by collecting tourists’ cognitive data about them. The accuracy of the established 3D animation model is as low as 88.02% and as high as 97.21%, the total fluctuation of completeness ranges from 84.93% to 96.43%, and the time used for modeling basically stays below 2.9h. It has a high awareness of its natural landscape (26.44%), humanistic landscape (37.33%), folk culture (20.57%), service facilities (9.23%), characters (4.40%), and specialty food (2.03%). The study provides a theoretical and practical foundation for communicating images of ancient villages.

Coarse Alignment Methodology of Point Cloud Based on Camera Position/Orientation Estimation Model.

This study presents a methodology for the coarse alignment of light detection and ranging (LiDAR) point clouds, which involves estimating the position and orientation of each station using the pinhole camera model and a position/orientation estimation algorithm. Ground control points are obtained using LiDAR camera images and the point clouds are obtained from the reference station. The estimated position and orientation vectors are used for point cloud registration. To evaluate the accuracy of the results, the positions of the LiDAR and the target were measured using a total station, and a comparison was carried out with the results of semi-automatic registration. The proposed methodology yielded an estimated mean LiDAR position error of 0.072 m, which was similar to the semi-automatic registration value of 0.070 m. When the point clouds of each station were registered using the estimated values, the mean registration accuracy was 0.124 m, while the semi-automatic registration accuracy was 0.072 m. The high accuracy of semi-automatic registration is due to its capability for performing both coarse alignment and refined registration. The comparison between the point cloud with refined alignment using the proposed methodology and the point-to-point distance analysis revealed that the average distance was measured at 0.0117 m. Moreover, 99% of the points exhibited distances within the range of 0.0696 m.

Feature Relation Guided Cross-View Image Based Geo-Localization

The goal of cross-view image based geo-localization is to determine the location of a given street-view image by matching it with a collection of geo-tagged aerial images, which has important applications in the fields of remote sensing information utilization and augmented reality. Most current cross-view image based geo-localization methods focus on the image content and ignore the relations between feature nodes, resulting in insufficient mining of effective information. To address this problem, this study proposes feature relation guided cross-view image based geo-localization. This method first processes aerial remote sensing images using a polar transform to achieve the geometric coarse alignment of ground-to-aerial images, and then realizes local contextual feature concern and global feature correlation modeling of the images through the feature relation guided attention generation module designed in this study. Specifically, the module includes two branches of deformable convolution based multiscale contextual feature extraction and global spatial relations mining, which effectively capture global structural information between feature nodes at different locations while correlating contextual features and guiding global feature attention generation. Finally, a novel feature aggregation module, MixVPR, is introduced to aggregate global feature descriptors to accomplish image matching and localization. After experimental validation, the cross-view image based geo-localization algorithm proposed in this study yields results of 92.08%, 97.70%, and 98.66% for the top 1, top 5, and top 10 metrics, respectively, in CVUSA, a popular public cross-view dataset, and exhibits superior performance compared to algorithms of the same type.

DVL-Aided In-Motion Coarse Alignment for Underwater Vehicles With Latitude Uncertainty

To solve the in-motion alignment problem of underwater vehicles with latitude uncertainty, a Doppler velocity log (DVL)-aided coarse alignment method is proposed in the paper. The in-motion coarse alignment with latitude uncertainty is formulated as an optimization problem, and the objective function in quaternion-based velocity increment form is presented. Firstly, the coarse estimate of attitude quaternion is conducted to obtain a rough attitude reference through taking the position matrix as an unknown constant matrix. Secondly, the influences of vehicle's linear motion on the estimate of the attitude quaternion are analyzed in detail. Then the angular rate related to vehicle's linear velocity, which causes the large yaw error in the coarse estimation stage, is eliminated from the gyroscope outputs. Moreover, a more accurate latitude estimate and attitude quaternion are achieved by using the corrected gyroscope outputs into the determination of attitude quaternion, completely separating the linear motion from the vehicle's in-motion condition. The results of simulation and experiment demonstrate that the proposed method can overcome the interferences of vehicle's linear motion, and verify its effectiveness to solve the in-motion coarse alignment with latitude uncertainty.

A fast strapdown gyrocompassing algorithm based on INS differential errors

This paper presents an enhanced algorithm for inertial gyrocompassing using strapdown sensors, which performs faster than the other available ones. The proposed algorithm is based on differential errors in an inertial navigation system and requires only the output of the inertial measurement unit while extracting and compensating for the inertial sensor errors. After eliminating the error of the inertial sensors, which is accomplished swiftly, the coarse alignment algorithm performs with error-compensated sensors, and the true north is extracted accurately. The number of non-observable parameters of the algorithm is equal to that of the fine alignment algorithm; therefore, its accuracy is the same as that of a well-tuned fine alignment. Numerical simulations and lab experiments demonstrate that the proposed method performs heading estimation in the time required to perform the coarse alignment, which is faster than the existing fine alignment algorithms.

Automatic Multiview Alignment of RGB-D Range Maps of Upper Limb Anatomy.

Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm 3D scanning, such as needing multiple views, stability requirements, and optical undercuts. While bulky and expensive multi-camera systems have been used in previous research, this study explores the feasibility of using multiple consumer RGB-D sensors for scanning human anatomies. The proposed scanner comprises three Intel® RealSenseTM D415 depth cameras assembled on a lightweight circular jig, enabling simultaneous acquisition from three viewpoints. To achieve automatic alignment, the paper introduces a procedure that extracts common key points between acquisitions deriving from different scanner poses. Relevant hand key points are detected using a neural network, which works on the RGB images captured by the depth cameras. A set of forearm key points is meanwhile identified by processing the acquired data through a specifically developed algorithm that seeks the forearm's skeleton line. The alignment process involves automatic, rough 3D alignment and fine registration using an iterative-closest-point (ICP) algorithm expressly developed for this application. The proposed method was tested on forearm scans and compared the results obtained by a manual coarse alignment followed by an ICP algorithm for fine registration using commercial software. Deviations below 5 mm, with a mean value of 1.5 mm, were found. The obtained results are critically discussed and compared with the available implementations of published methods. The results demonstrate significant improvements to the state of the art and the potential of the proposed approach to accelerate the acquisition process and automatically register point clouds from different scanner poses without the intervention of skilled operators. This study contributes to developing effective upper limb rehabilitation frameworks and personalized biomedical applications by addressing these critical challenges.

Reflection-based vision guide method for coarse alignment of vector measurement during the satellite assembly, integration, and test process.

Vector measurement is a vital measurement item during the satellite assembly, integration, and test (AIT) process. With the increasing popularity of commercial spaceflight, the development cycle of a satellite is shorter, and the number of satellites has been growing rapidly. The traditional on-site vector measurement method is inefficient and significantly affects the development cycle of the satellite. Therefore, it is of utter importance to propose an online high-precision automatic vector measurement system. The most challenging step of the online automatic vector measurement is coarse alignment because a cubic prism must be identified, and the normal direction of its surface must be calculated at a certain precision in the unstructured environment during the coarse alignment step. A reflection-based vision guide method was proposed to identify and calculate the normal direction of the cubic prism. The working principle and advantage of the proposed vision guide system were described in detail. What is more, the calibration and calculation methods of the proposed vision guide system were also presented. Finally, experiments were conducted to verify the effectiveness of the proposed method.

Coarse Alignment Based on Weighted Observation Vectors in Mooring State for Surface Targets

Coarse Alignment Based on Weighted Observation Vectors in Mooring State for Surface Targets

Automatic 3D Postoperative Evaluation of Complex Orthopaedic Interventions.

In clinical practice, image-based postoperative evaluation is still performed without state-of-the-art computer methods, as these are not sufficiently automated. In this study we propose a fully automatic 3D postoperative outcome quantification method for the relevant steps of orthopaedic interventions on the example of Periacetabular Osteotomy of Ganz (PAO). A typical orthopaedic intervention involves cutting bone, anatomy manipulation and repositioning as well as implant placement. Our method includes a segmentation based deep learning approach for detection and quantification of the cuts. Furthermore, anatomy repositioning was quantified through a multi-step registration method, which entailed a coarse alignment of the pre- and postoperative CT images followed by a fine fragment alignment of the repositioned anatomy. Implant (i.e., screw) position was identified by 3D Hough transform for line detection combined with fast voxel traversal based on ray tracing. The feasibility of our approach was investigated on 27 interventions and compared against manually performed 3D outcome evaluations. The results show that our method can accurately assess the quality and accuracy of the surgery. Our evaluation of the fragment repositioning showed a cumulative error for the coarse and fine alignment of 2.1 mm. Our evaluation of screw placement accuracy resulted in a distance error of 1.32 mm for screw head location and an angular deviation of 1.1° for screw axis. As a next step we will explore generalisation capabilities by applying the method to different interventions.

High-precision moiré alignment method using centrosymmetric grating for 3D interlayer interconnect

The fabrication of integrated circuits in the future will rely heavily on high-precision three-dimensional interlayer interconnect alignment technology. In this study, we explore a controllable and clear-cut alignment method based on moiré centrosymmetric grating marks that can achieve fine alignment in the X-Y-θ direction simultaneously with only a single alignment mark without additional coarse alignment mark. Compared with the traditional peak-intensity-based detection method, circumferential alignment of the order of 10−4 deg and radial alignment of less than 10 nm can be achieved, and its fine alignment accuracy is improved by nearly two orders of magnitude by using the proposed local moiré phase alignment method. With further optimization of the parameters, sub-nanometer alignment is expected to be achieved. Moreover, our measurement system is simpler and more stable. Both simulation and experimental results verify the feasibility of the proposed method. The uncertainty analysis of the results is also reported.

Towards robust registration of heterogeneous multispectral UAV imagery: A two-stage approach for cotton leaf lesion grading

Multiple source images acquired from diverse sensors mounted on unmanned aerial vehicles (UAVs) offer valuable complementary information for ground vegetation analysis. However, accurately aligning heterogeneous UAV images poses challenges due to differences in geometry, intensity, and noise resulting from varying imaging principles. This paper presents a two-stage registration method aimed at fusing visible RGB and multispectral images for cotton leaf lesion grading. The coarse alignment stage utilizes Scale Invariant Feature Transform (SIFT), while the refined alignment stage employs a novel correlation coefficient-based template matching. The proposed method first employs the EfficientDet network to detect infected cotton leaves with lesions in RGB images. Subsequently, lesion leaves in multiple spectral imagery (red, green, red edge, and near-infrared bands) are located using the perspective transformation matrix derived from SIFT and the coordinates of lesion leaves in RGB images. Refined registration between RGB and multispectral imagery is achieved through template matching with the new correlation coefficient. The registered reflectance data from the different spectral bands and RGB components are utilized to classify pixels in each infected leaf into lesion, healthy, and soil parts. The lesion grade is determined based on the ratio of lesion pixels to the total corresponding leaf area. Experimental results, compared with manual assessment, demonstrate a lesion leaves detection model with a mAP@0.5 of 91.01% and a leaf lesion grading accuracy of 92.01%. These results validate the suitability of the proposed method for UAV RGB and multispectral image registration, enabling automated cotton leaf lesion grading.

Novel Method for Coarse Alignment of Strapdown Ins On Oscillatory Base

Novel Analytic Coarse Alignment Method is presented for Strapdown Inertial Navigation Systems (SDINS) alignment on oscillatory base. Oscillatory base alignment is conducted by building two vectors derived from gravity and angular velocity information provided by the strapdown INS under oscillatory motion and comparing them with similar vectors built using the local level reference frame information. The theory and algorithms are detailed in the paper and several simulation test results are compiled for various kinds of oscillatory motion.The proposed algorithm could be used for sea based INS applications.

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.

Data-driven denoising of stationary accelerometer signals

Modern navigation solutions are largely dependent on the performances of the standalone inertial sensors, especially at times when no external sources are available. During these outages, the inertial navigation solution is likely to degrade over time due to instrumental noises sources, particularly when using consumer low-cost inertial sensors. Conventionally, model-based estimation algorithms are employed to reduce noise levels and enhance meaningful information, thus improving the navigation solution directly. However, guaranteeing their optimality often proves to be challenging as sensors performance differ in manufacturing quality, process noise modeling, and calibration precision. In the literature, most inertial denoising models are model-based when recently several data-driven approaches were suggested primarily for gyroscope measurements denoising. Data-driven approaches for accelerometer denoising task are more challenging due to the unknown gravity projection on the accelerometer axes. To fill this gap, we propose several learning-based approaches and compare their performances with prominent denoising algorithms, in terms of pure noise removal, followed by stationary coarse alignment procedure. Based on the benchmarking results, obtained in field experiments, we show that the learning-based models outperform traditional signal processing filtering in terms of pure inertial signal reconstruction. Moreover, they are shown to improve angular errors by one order of magnitude, given a navigation-related task.

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.

A Polar Moving Base Alignment Based on Backtracking Scheme

In the polar region, the gravity vector and Earth’s rotation vector tend to be in the same direction, leading to a slower convergence speed and longer alignment time of the moving base alignment. When the alignment time is short, the alignment cannot converge, resulting in low azimuth accuracy. To address this issue, we propose a polar moving base alignment method based on a backtracking scheme. Notably, this work first derives a polar coarse alignment method with the inertial frame based on the transverse Earth model. On this basis, we designed a polar coarse alignment method based on a backtracking scheme and optimized the data storage scheme. Then, a backward navigation algorithm based on the transverse inertial navigation mechanical arrangement scheme was derived, and a polar fine alignment method based on a backtracking scheme was designed. Semi-physical simulation experiments showed that the alignment algorithm based on a backtracking scheme could converge in the 180 s with high alignment accuracy, which is 70% faster than the current polar moving base alignment method.

Rapid Initial Heading Alignment for MEMS Land Vehicular GNSS/INS Navigation System

For the low-cost microelectromechanical system (MEMS) inertial navigation system (INS) and global navigation satellite system (GNSS) integrated system in land vehicular applications, quick and accurate coarse alignment is still a challenge, especially for a host vehicle that moves at low speed under low dynamics. In this paper, we propose an in-motion coarse initial heading alignment algorithm for the low-cost MEMS INS aided by the GNSS, which is specifically designed for land vehicular conditions. The principle of the proposed method is straightforward when considered in the context of trajectory matching: the MEMS can compute a relative vehicle trajectory through a dead reckoning (DR) calculator using the gyro-derived attitude solution and the travel distance, and the GNSS can provide an absolute vehicle trajectory through its positioning solution. The initial heading is then computed by comparing these DR-indicated and GNSS-indicated trajectories. The proposed algorithm is verified using a civilian vehicle, a wheeled robot, and an agricultural tractor under different motion and dynamic conditions. The results show that the initial heading could be quickly determined with an accuracy of 0.25°, 0.6°, and 1.6° at a 98.6% confidence level within 5 s for the car, robot and tractor tests, respectively.

An Improved In-Motion Coarse Alignment Method for SINS/GPS Integration with Initial Velocity Error Suppression.

The integrated system with the strapdown inertial navigation system (SINS) and the global positioning system (GPS) is the most popular navigation mode. It has been used in many navigation fields. Before the integrated system works properly, it must determine the initial attitude for SINS. In SINS/GPS-integrated systems, the navigational velocity can be used to carry out the initial alignment when the system is installed in the in-motion vehicle. However, the initial velocity errors are not considered in the current popular in-motion alignment methods for SINS/GPS integration. It is well-known that the initial velocity errors must exist when the initial velocity is obtained from the GPS outputs. In this paper, an improved method was proposed to solve this problem. By analyzing the original observation vectors in the in-motion coarse alignment method, an average operation was used to construct the intermediate vectors, and the new observation vector can be calculated by subtracting the intermediate vector from the original observation vector. Then, the initial velocity errors can be eliminated from the newly constructed observation vector. Thus, the interferences of the initial velocity errors for the initial alignment process can be suppressed. The simulation and field tests are designed to verify the performance of the proposed method. The tests results showed that the proposed method can obtain the higher accurate results than the current methods when the initial velocity is considered. Additionally, the results of the proposed method were similar to the current methods when the initial velocity errors were not considered. This shows that the initial velocity errors were eliminated effectively by the proposed method, and the alignment accuracy were not decreased.

A Polar Integrated Alignment Assisted by DVL Under Large Azimuth Misalignment

The gravity tends to coincide with the earth’s rotation vector in the polar region, degrading the coarse alignment azimuth of the strapdown inertial navigation system (SINS) to a large misalignment. Under a large azimuth misalignment, the linear error model inevitably renders unacceptable theoretical errors in the integrated alignment. In addition, polar region challenge such sensors as a star sensor of celestial navigation, which fails to provide high-precision attitude reference for integrated alignment. Therefore, in allusion to the misalignment mentioned above based on SINS for the polar region, an integrated alignment algorithm is proposed, assisted by the Doppler Velocity Log (DVL). Notably, the work first investigates the extent to which azimuth accuracy deteriorates in the polar region. On the premise of large azimuth misalignment, the nonlinear error model under the transverse frame is derived from the ellipsoid earth model. Additionally, the state and measurement models are constructed, combined with the velocity of DVL as an observation. An adaptive measurement variance matrix is designed for the unscented Kalman filter (UKF) to estimate the attitude error. Eventually, the semi-physical experiments demonstrate that the proposed algorithm significantly improves the initial alignment accuracy of the polar region.

A Coarse Alignment Method Based on Vector Observation and Truncated Vectorized $\boldsymbol{\kappa}$-Matrix for Underwater Vehicle

This paper focuses on the coarse alignment problem for the strapdown inertial navigation system (SINS) applied to the underwater vehicle. The coarse alignment is one of the crucial technologies of underwater navigation and determines whether SINS can work normally. However, the traditional coarse alignment methods suffer from the complicated noises hidden in the inertial sensors’ outputs. To solve this problem, a new coarse alignment algorithm based on vector observation and truncated vectorized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix is proposed in this paper. Different from previous methods based on the optimal quaternion, the proposed algorithm makes the matrix state-space formulae of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix vectorized, and truncates the resulting state vectors by implementing the linear dependence within the components of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix. On one hand, based on the vectorized and truncated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix, a linear reduced model of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix can be applied to the estimation process, and the measurement errors of inertial sensors can be easily restrained using the estimation algorithm. On the other hand, the proposed approach makes up for the drawbacks of the Optimal-REQUEST algorithm which has a conservative covariance matrix and scalar gain. The simulation test and practical experiment demonstrate that the proposed approach can reduce random noise effectively. Therefore, the performance of coarse alignment algorithm can be improved more enormously than the traditional algorithms.