Contour Matching Algorithm Research Articles

Urban Building Height Extraction from Gaofen-7 Stereo Satellite Images Enhanced by Contour Matching

The traditional method for extracting the heights of urban buildings involves utilizing dense matching algorithms on stereo images to generate a digital surface model (DSM). However, for urban buildings, the disparity discontinuity issue that troubles the dense matching algorithm makes the elevations of high-rise buildings and the surrounding areas inaccurate. The occlusion caused by trees in greenbelts makes it difficult to accurately extract the ground elevation around the building. To tackle these problems, a method for building height extraction from Gaofen-7 (GF-7) stereo images enhanced by contour matching is presented. Firstly, a contour matching algorithm was proposed to extract accurate building roof elevation from GF-7 images. Secondly, a ground filtering algorithm was employed on the DSM to generate a digital elevation model (DEM), and ground elevation can be extracted from this DEM. The difference between the rooftop elevation and the ground elevation represents the building height. The presented method was verified in Yingde, Guangzhou, Guangdong Province, and Xi’an, Shaanxi Province. The experimental results demonstrate that our proposed method outperforms existing methods in building height extraction concerning accuracy.

Rail profile matching method based on stepped calibration plate and improved SURF algorithm

In order to avoid the calibration plane and laser plane do not coincide and the rail profile is difficult to match dynamically, a stepped calibration plate is made, and a rail profile matching method based on the improved SURF algorithm and random sample consensus algorithm (RANSAC) is proposed. Firstly, a stepped rail profile calibration plate is designed and fabricated, which can directly calibrate the laser plane, avoiding the problem that the calibration plane and the line laser plane do not coincide completely in practical application. In the calibration process, collect the optimal control points and calibrate the image by polynomial transformation and bilinear interpolation, then complete contour rough matching using SURF algorithm. During the SURF algorithm, construct the matching constraint of the center of the orbit waist to delete part of the false matches. Finally, the improved RANSAC algorithm is used to filter the wrong matches, achieving precise contour matching. The experimental results show that compared with traditional contour matching methods, the improved contour matching algorithm has higher accuracy and stronger robustness.

An INS/Geomagnetic Integrated Navigation Method for Coarse Estimation of Positioning Error and Search Area Adaption Applied to High-Speed Aircraft

The geomagnet aided inertial navigation algorithm has become one of the current research hotspots in the field of aircraft navigation. It can overcome the shortcomings of pure inertial navigation error accumulation over time and achieve long-term, autonomous, and all-weather navigation. The traditional iterative closest contour point (ICCP) algorithm, however, requires high sensor performance, has poor tolerance to noise, and has a complex algorithm with poor real-time performance. The timeliness of the traditional magnetic contour matching (MAGCOM) algorithm is related to the search area, the matching search area is fixed, and the algorithm is inflexible. To address the above problems, this article proposes a geomagnetic navigation method based on coarse estimation of navigation error and search area adaption. When the aircraft flies into the geomagnetic database region, the navigation error is roughly estimated based on the error characteristics of the inertial navigation system. Considering the influence of noise and uncertainty, three times the value of the estimated error is used as the edge length of the geomagnetic matching search area, and geomagnetic matching is performed for positioning. The product of velocity and time provided by the inertial navigation system is used as a constraint to determine whether the geomagnetic matching results are abnormal. If multiple consecutive geomagnetic matching results are normal, the navigation system automatically reduces the matching search area and search step to improve the matching accuracy and timeliness. Simulation experiments show that the accuracy of the adaptive search area geomagnetic matching method proposed in this article can reach within 200 m.

Improving Matching Efficiency and Out-of-domain Reliability of Underwater Gravity Matching Navigation Based on a Novel Soft-margin Local Semicircular-domain Re-searching Model

This paper mainly studies the improvement of the efficiency and out-of-domain reliability of gravity matching navigation for underwater vehicles. To overcome the traversal low-efficiency problem of the traditional terrain contour matching (TERCOM) algorithm and improve the positioning reliability of its out-of-domain mismatches, a novel soft-margin local semicircular-domain re-searching model (SLSR) is proposed by integrating the soft-margin circular grid matching (SCGM) mechanism and the local semicircular grid re-matching (LSGR) mechanism. SCGM uses three times the inertial navigation cumulative error and adds the unit grid resolution as the soft margin boundary to generate the soft-margin circular domain, which contributes to reducing in-domain matching grid points and enhancing the matching efficiency of algorithms. Then the optimal matching position in this soft-margin circular domain is obtained by using the optimization principle of matching indices. LSGR is triggered when the optimal matching position of SCGM is located near the soft-margin circular-domain boundary. It employs this optimal matching point as the center and the unit inertial navigation error as the radius to recreate the semicircular local re-searching matched grid domain (termed as semicircular domain). Moreover, the optimal matching point in this semicircular domain is obtained by the matching index optimization principle, and then it is compared and updated to obtain the final best matching position of SLSR. The simulation results show that SCGM and LSGR of the proposed SLSR method can effectively improve the matching efficiency and out-of-domain matching reliability of underwater navigation, respectively. Under the same testing conditions for the tracking starting points from three gravity regions, the number of out-of-domain mismatches of SLSR, compared with TERCOM, are lower up to 92.68%, 90.24% and 98.62%, while the average matching accuracies are relatively improved by 88.37%, 85.48% and 83.66%, which verifies the validity and feasibility of the proposed SLSR model on improving the efficiency and out-of-domain reliability of underwater gravity matching navigation.

Improving Matching Accuracy of Underwater Gravity Matching Navigation Based on Iterative Optimal Annulus Point Method with a Novel Grid Topology

In this study, we improve the matching accuracy of underwater gravity matching navigation. Firstly, the Iterative Optimal Annulus Point (IOAP) method with a novel grid topology is proposed for breaking through the inherent grid structure limit of the canonical gravity matching algorithm and enhancing its underwater gravity matching accuracy. The theory of IOAP is as follows: (1) small-annulus matching and positioning mechanism on the tracking starting point is developed by employing the starting point and drift error of the INS (Inertial Navigation System), the fixed rotation angle, etc. The optimal matching location of the starting point is obtained by matching and comparing the matched points in this small-annulus grid, which contributes to heightening the initial-position error insensitivity of the algorithms. (2) Variable-angle three-layer annulus matching and positioning mechanisms on the tracking ending point were constructed by using the optimal matching location of the starting point and combining the tracking direction-and-distance information of the INS and the cumulative drift error, etc. It is used to generate the annulus matching points with the ring-type grid topology. (3) The optimal matching position of the ending point in this annulus is obtained by iteratively calculating the evaluation index value of the matching points and following the evaluation index optimal rule. Secondly, we comprehensively consider the main performance evaluation indexes of the underwater gravity matching algorithms, such as the statistical indicators of the matching accuracy, the average matching time and the matching success rate, and take them as a basis of the pros and cons of the matching analysis. Furthermore, under conditions that include different scale searching regions or different reference-angle ring radii, the statistical results verify that the IOAP had a different matching ability and better robustness. Finally, several trajectories with the starting points from different areas and the ending points in different gravity ranges are tested and compared to carry out the numerical simulations. These results indicate that the IOAP has many advantages, such as a high matching accuracy and strong positioning applicability in different gravity regions. Compared with the TERCOM (terrain contour matching algorithm), its average matching accuracy was the highest, increased by 40.39%.

A novel adaptive slicing algorithm based on ameliorative area ratio and accurate cusp height for 3D printing

PurposeAdaptive slicing is a key step in three-dimensional (3D) printing as it is closely related to the building time and the surface quality. This study aims to develop a novel adaptive slicing method based on ameliorative area ratio and accurate cusp height for 3D printing using stereolithography (STL) models.Design/methodology/approachThe proposed method consists of two stages. In the first stage, the STL model is sliced with constant layer thickness, where an improved algorithm for generating active triangular patches, the list is developed to preprocess the model faster. In the second stage, the model is first divided into several blocks according to the number of contours, then an axis-aligned bounding box-based contour matching algorithm and a polygons intersection algorithm are given to compare the geometric information between several successive layers, which will determine whether these layers can be merged to one.FindingsSeveral benchmarks are applied to verify this new method. Developed method has also been compared with the uniform slicing method and two existing adaptive slicing methods to demonstrate its effectiveness in slicing.Originality/valueCompared with other methods, the method leads to fewer layers whilst keeping the geometric error within a given threshold. It demonstrates that the proposed slicing method can reach a trade-off between the building time and the surface quality.

Research on Machine Learning Algorithm Based on Contour Matching Modal Matrix

For the problem of contour matching modal matrix optimization, a combination of contour matching and gradient algorithm is proposed as an optimization search method. The basic idea of regression estimation and machine learning set is the same, both of them reconstruct the estimation of samples by estimating the machine learning clustering coefficients, and the main difference is the different models chosen. Although contour matching has the advantage of global optimization search, it has the defects of easy prematureness and poor local optimization search performance. Therefore, this paper combines it with the gradient algorithm, which not only speeds up the search in the gradient algorithm and ensures that the method converges to the global optimal solution, but also achieves the global convergence of the method and the high efficiency of the computational speed by keeping the optimal solution of the iterative process.

Comparison of geomagnetic aided navigation algorithms for hypersonic vehicles

In this paper, we simulate, verify, and compare the performance of three classical geomagnetic matching aided navigation algorithms to assess their applicability to hypersonic vehicle navigation. Firstly, we introduce the various sources of the geomagnetic field. Secondly, we describe the principles and processes of the geomagnetic contour matching (MAGCOM) algorithm, iterative closest contour point (ICCP) algorithm, and Sandia inertial magnetic aided navigation (SIMAN) algorithm. Thirdly, we discuss the principles of inertial/geomagnetic integrated navigation, and propose the state and observation equations of integrated navigation. Finally, we perform a simulation of inertial/geomagnetic integrated navigation on the hypersonic boost-glide vehicle trajectory. The simulation results indicate that the real-time performance of the SIMAN algorithm can be optimized such that the matching accuracy is higher than that of the other two algorithms. At the same time, the SIMAN algorithm can achieve better stability, and though the amount of measurement noise can be larger, it can still achieve good positioning accuracy.

Human face detection and recognition using contour generation and matching algorithm

Face detection, monitoring of human face and recognition is a high-quality venture in safety, surveillance and regulation enforcement systems with greater accuracy. There are many motives at the back of in this trouble. Which include function of the human face; pose version, negative environmental conditions, terrible lightning and picture tilt etc,. So we must locate a few feasible solutions to clear up those issues by means of imposing a brand new system. Our proposed venture might be improving the issues on Face detection, monitoring of human face and recognition issues.

Retraction: Xiaohong Zhu, Zili Yang and Tabharit Zoubir, Research on the matching algorithm for heterologous image after deformation in the same scene

The existing contour matching algorithm is difficult to deal with the local contour matching problem of the heterologous image in same scene, and the different shooting angles or non approximate transformation will cause some deformation. In this regard, this paper proposes a matching algorithm based on image segmentation. According to the characteristics of the image, the idea of extracting the outline by first image segmentation and then extracting the contour is used to extract the coarse contour. the feature space based on mean gray, gray variance and entropy is constructed, to represent the characteristics of different material object in the image; then the initial clustering number and clustering center optimized by the ant colony algorithm is used to make fuzzy clustering of the image feature space; Canny operator is used for the edge detection, so the coarse contour images is obtained. The PNP algorithm is used to combine the same name points obtained from the initial matching to calculate the angle transformation parameters between the two images. By inverse operation, the matching contour in the two images is corrected to the same view. The experimental results show that the proposed method can effectively improve the precision of the segmentation and the matching precision of the heterogenous image.

Application of Particle Swarm Optimization Algorithm in Geomagnetic Matching Navigation

In order to improve the positioning accuracy of the traditional geomagnetic matching algorithm, a new geomagnetic matching algorithm based on particle swarm optimization is proposed. The algorithm plans the search range of the real position centering on the measurement position of the reference navigation system. Extracting the corresponding geomagnetic data from the reference geomagnetic map, the particle swarm optimization algorithm is then introduced into the matching region search, and the obtained geomagnetic profile values are taken as particles in the particle population. On this basis, the normalized product correlation function is used as the particle fitness function, comparing the similarity between the reference sub graph and the real-time graph profile by the maximum fitness measure, Finally, through simulation analysis, the matching precision and matching time of matching algorithm based on ICP and PSO are compared. The simulation results show that the matching effect based on PSO algorithm is better than the traditional ICP algorithm. Although the matching time is slightly longer than the ICP algorithm, it meets the real-time requirements. Geomagnetic correlation matching algorithm is an extremely important type of algorithm in geomagnetic aided navigation algorithm. The current research focuses on the improvement of the traditional Terrain Contour Matching (TERCOM) algorithm metric function and the introduction of new metric functions and various ICP geomagnetic matching algorithms [1-4].The geomagnetic correlation matching algorithm of the aircraft not only requires high precision and high reliability, but also the execution efficiency of the algorithm is one of the important contents of the research. Based on the research of traditional geomagnetic correlation matching algorithm, this paper introduces particle swarm optimization algorithm into geomagnetic correlation matching algorithm. From the aspects of similarity measure function, geomagnetic matching feature quantity and search strategy, a new method of geomagnetic correlation matching for aircraft is proposed.

Geomagnetic matching navigation algorithm based on robust estimation

The outliers in the geomagnetic survey data seriously affect the precision of the geomagnetic matching navigation and badly disrupt its reliability. A novel algorithm which can eliminate the outliers influence is investigated in this paper. First, the weight function is designed and its principle of the robust estimation is introduced. By combining the relation equation between the matching trajectory and the reference trajectory with the Taylor series expansion for geomagnetic information, a mathematical expression of the longitude, latitude and heading errors is acquired. The robust target function is obtained by the weight function and the mathematical expression. Then the geomagnetic matching problem is converted to the solutions of nonlinear equations. Finally, Newton iteration is applied to implement the novel algorithm. Simulation results show that the matching error of the novel algorithm is decreased to 7.75% compared to the conventional mean square difference (MSD) algorithm, and is decreased to 18.39% to the conventional iterative contour matching algorithm when the outlier is 40nT. Meanwhile, the position error of the novel algorithm is 0.017° while the other two algorithms fail to match when the outlier is 400nT.

Robust methods of magnetic navigation of marine robotic vehicles

This paper describes the development of robust estimation methods of magnetic navigation for small, affordable underwater robotic vehicles. Relying on prior work by the authors, the paper proposes a particle filter navigation algorithm which is integrated with other position estimation methods to increase the robustness of the estimation process. The new techniques embedded in the navigation particle filter include an adaptive-likelihood particle filter and a magnetic contour matching algorithm implemented in the Fourier domain to achieve superior computation speeds. The performance of the navigation methods proposed is assessed through a series of tests which exploit real data acquired in the water with a marine robotic vehicle. The results obtained illustrate the high potential of the approach for magnetic navigation of autonomous underwater vehicles.

Application of acoustic image processing in underwater terrain aided navigation

This paper proposes an idea that treats multi-beam bathymetric sonar data as images for underwater terrain aided navigation. Bathymetric measurements are converted into the form of digital images by interpolation at the beginning of the process. Next, image texture features are obtained and integrated into terrain navigability parameters to define an underwater terrain feature vector. Then, by scanning the underwater terrain map using real-time data, the best match between their feature vectors are considered as the position estimation. Simulation results suggest that this method is reliable when signal to noise ratio is under 2dB. In addition, the method is shown to be robust to image rotation as large as 20° and scale variance as small as 50×50 pixels, which means it is not sensitive to direction errors and the distance between the underwater vehicle and the bottom may vary. When image resolution is 1m2/pixel and scanning step is 1m/move, accuracy of the method can be as high as 1m in suitable terrains. In comparison with terrain contour matching algorithm, performance of the proposed method is more stable when direction error is bigger than 10° and the accuracy is approximately 50% better while noise and scale vary in the real-time images.

An Improved TERCOM-Based Algorithm for Gravity-Aided Navigation

Gravity-aided inertial navigation is a leading issue in the application of autonomous underwater vehicle. An improved gravity matching algorithm, based on the principle of the terrain contour matching (TERCOM) algorithm, is proposed. The matching algorithm applies the shortest path algorithm to increase update frequency. In addition, the positioning error can be limited due to the novel correlation analysis method. Compared with existing algorithms, the improved TERCOM algorithm has better real-time performance, positioning accuracy, and reduced calculation burden. The reliability and the accuracy of the algorithm are verified via simulation tests.

A Novel Terrain-Aided Navigation Algorithm Combined With the TERCOM Algorithm and Particle Filter

In order to overcome the shortcomings of the Beihang University of Aeronautical and Astronautics inertial terrain-aided navigation II (BITAN II) algorithm, which is based on extended Kalman filtering, a novel algorithm combined with the terrain contour matching (TERCOM) algorithm and particle filter is proposed. Compared with BITAN II, the proposed algorithm involves a new algorithm framework, acquisition mode, and mode control logic. In particular, the acquisition mode employs TERCOM to implement a consistency voting algorithm, which can quickly and accurately obtain an initial position given in a large initial position error; the mode control logic is designed using the instantaneous information of strapdown inertial navigation system, such that false fix can be effectively detected, and security and accuracy can be guaranteed as well. Experiments are carried out using the digital elevation model and real flight data, and show that, in comparison with BITAN II, the algorithm presented in this paper is reliable and achieves relatively high positioning accuracy in the case of large initial position errors and large altimeter measurement noises.

Precision robotic deburring based on force control for arbitrarily shaped workpiece using CAD model matching

Surface finishing processes such as deburring are crucial for ensuring the quality of a workpiece and human safety by removing burrs. However, deburring involves excessive noise, dust, and vibration, which can be harmful to human workers. Thus, there has been extensive research into the use of robots to perform deburring instead of human workers. In robotic deburring, the precise tracking of the contour of an arbitrarily shaped workpiece is of major concern for precision deburring. In this study, to achieve precision deburring, a tool-path modification method based on a computer-aided design (CAD) model and direct teaching is proposed taking into consideration the position/orientation errors of the workpiece. In addition, based on this trajectory, impedance control is used to avoid applying an excessive contact force and a virtual wall is adopted to improve the force-control performance. Without knowing the position/orientation of the workpiece, the optimal deburring trajectory can be generated by matching the extracted tool path from the CAD model to the teaching points. From the simulations of the tool path modification method using an iterative closest point (ICP)-based contour matching algorithm and a series of experiments on robotic deburring, the performance of the proposed method was verified.

Robust inertial terrain aided navigation algorithm

The traditional terrain contour matching (TERCOM) algorithm has worse reliability when velocity error or course error is larger. The extend Kalman filtering (EKF) algorithm based BUAA inertial terrain aided navigation (BITAN) algorithm fails to correctly position, leading to a decline of robustness when large initial position error or altimeter noise error occurs. In this paper, we introduce a Robust BUAA inertial terrain aided navigation (RBITAN) algorithm, which is an improved algorithm of BITAN. In the RBITAN algorithm a searching mode approach is designed by the statistic properties of mean absolute difference algorithm, mean square difference algorithm and cross correlation algorithm. The RBITAN gathers the advantages of both the TERCOM algorithm and the BITAN algorithm, and it adopts EKF based BITAN algorithm as the tracking approach. The algorithm is verified by both real digital altitude model and flight-test data. Compared with the BITAN algorithm, the RBITAN algorithm is robust, for it can achieve accurate positioning and tolerate large initial position error or altimeter noise error.

An Improved Geomagnetic Matching Algorithm Using Rotation Angle Search Technique

The accumulation course angle error of inertial navigation system will decrease the accuracy and reliability of an geomagnetism aided inertial navigation system using a geomagnetic contour matching algorithm. To improve the matching accuracy, the matching track and true track should be as parallel as possible. An improved geomagnetic matching algorithm is presented by introducing rotation angle search technique. To reduce the computation burden, improve operation efficiency and reduce false matching probability, a new search area determination method is proposed, which redefines the search region and reduces the search range. Simulation results demonstrate the effectiveness of the proposed algorithm and the improvement in the matching accuracy.

Simulation research on a minimum root-mean-square error rotation-fitting algorithm for gravity matching navigation

Gravity/inertial combination navigation is a leading issue in realizing passive navigation onboard a submarine. A new rotation-fitting gravity matching algorithm, based on the Terrain Contour Matching (TERCOM) algorithm, is proposed in this paper. The algorithm is based on the principle of least mean-square-error criterion, and searches for a certain matched trajectory that runs parallel to a trace indicated by an inertial navigation system on a gravity base map. A rotation is then made clockwise or counterclockwise through a certain angle around the matched trajectory to look for an optimal matched trajectory within a certain angle span range, and through weighted fitting with another eight suboptimal matched trajectories, the endpoint of the fitted trajectory is considered the optimal matched position. In analysis of the algorithm reliability and matching error, the results from simulation indicate that the optimal position can be obtained effectively in real time, and the positioning accuracy improves by 35% and up to 1.05 nautical miles using the proposed algorithm compared with using the widely employed TERCOM and SITAN methods. Current gravity-aided navigation can benefit from implementation of this new algorithm in terms of better reliability and positioning accuracy.