Unified Coordinate Research Articles

Distributed measurement system calibration method with automatic networking mechanism

This paper presents a new method that balances accuracy and efficiency in the process of coordinate unification for distributed measurement systems. The method unifies the local coordinate systems of any two measuring units by installing a measuring point on the unit as a marker point and measuring the marker points between them in the working area, combined with the distance between the marker points. Moreover, it enables automatic networking of newly added or moved measurement units with the original measurement network, thereby further improving the efficiency of the dynamic measurements made using the system. The key factors affecting the accuracy of calibration achieved using this method were analysed via simulations. The results indicate that the accuracy of length measurement using the proposed methods is within 0.1 mm m−1 when the measurement units are distributed among the four corners of a rectangle with an area of 50 m2 or more. The experiments indicate that four measurement units calibrated using this method over an area of 35 m2 achieve an accuracy of within 0.2 mm m−1 and 0.1 mm standard deviations for length and three-dimensional coordinate measurements, respectively. Compared with traditional coordinate-unification methods, the proposed method simplifies the calibration process, improves the calibration efficiency by more than ten times, and significantly reduces the calibration time. It is universally applicable in the calibration of large-space distributed measurement systems.

Coordinate unification and data fusion for composite measurement with contact and white light interferometric probes

Coordinate unification and data fusion for composite measurement with contact and white light interferometric probes

S-QRD-ELM: Scalable QR-Decomposition-Based Extreme Learning Machine Engine Supporting Online Class-Incremental Learning for ECG-Based User Identification

User identification enables secure access to data and machines in smart factories. Compared with other modalities, ECG-based user identification is rising due to its intrinsic liveness proof and invulnerability to spoofing without contact. On the other hand, as new employees are registered at the factory, the ECG-based user identification system needs to be updated based on the new coming data. This scenario can be defined as an online class-incremental learning (O-CIL) problem. By exploiting hardware-software co-design, this work presents a Scalable QR-decomposition-based extreme learning machine (S-QRD-ELM) engine that can effectively and efficiently support O-CIL for ECG-based user identification. At the software level, we apply the concept of “the others” class and inversion-free QR-decomposition (QRD) recursive least squares to the S-QRD-ELM. This makes S-QRD-ELM achieve 79.7% higher accuracy in the O-CIL scenario compared with the neural network trained with back-propagation (BP-NN). At the hardware level, a one-dimensional diagonally-mapped linear array (1D-DMLA) is proposed to efficiently compute the QRD and back-substitution (BS) operations inside the S-QRD-ELM, reducing 98.5% of the silicon area. Moreover, the integrated processing element (PE) design with the unified COordinate Rotation DIgital Computer (u-CORDIC) further reduces 15.3% of the area and 22.4% of the power consumption. This engine is fabricated in 40nm CMOS technology with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.33\times1.33$</tex-math> </inline-formula> mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{2}$</tex-math> </inline-formula> die area. The chip achieves 0.02 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $</tex-math> </inline-formula> J/sample and 2.47 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $</tex-math> </inline-formula> J/sample inferencing and learning energy efficiency, respectively, which is 6.4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> and 28.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> than the state-of-the-art. To the best of our knowledge, the proposed highly energy-efficient S-QRD-ELM engine is the first chip to meet the requirements of O-CIL for ECG-based user identification.

Improved RANSAC Point Cloud Spherical Target Detection and Parameter Estimation Method Based on Principal Curvature Constraint.

Spherical targets are widely used in coordinate unification of large-scale combined measurements. Through its central coordinates, scanned point cloud data from different locations can be converted into a unified coordinate reference system. However, point cloud sphere detection has the disadvantages of errors and slow detection time. For this reason, a novel method of spherical object detection and parameter estimation based on an improved random sample consensus (RANSAC) algorithm is proposed. The method is based on the RANSAC algorithm. Firstly, the principal curvature of point cloud data is calculated. Combined with the k-d nearest neighbor search algorithm, the principal curvature constraint of random sampling points is implemented to improve the quality of sample points selected by RANSAC and increase the detection speed. Secondly, the RANSAC method is combined with the total least squares method. The total least squares method is used to estimate the inner point set of spherical objects obtained by the RANSAC algorithm. The experimental results demonstrate that the method outperforms the conventional RANSAC algorithm in terms of accuracy and detection speed in estimating sphere parameters.

Universal calibration for a ring camera array based on a rotational target.

Panoramic dynamic and static measurements of objects in the application of vision measurement are difficult due to the constraints of a camera field of view and multicamera calibration technology. This paper proposes a universal global calibration method for ring multicamera systems based on rotating target and multi-view vision technology. This method uses a rotating target to establish the relationship between ring multi-camera arrays, retrieves the coordinates of the target corners from the fields of view of different cameras, and combines them with the rotation angle to complete the coordinate unification of the system. The unification of coordinates is unaffected by the overlapping fields of view between cameras, and the number of cameras can be configured arbitrarily. The calibration accuracy, validity, and precision of the proposed method are verified through reprojection error, dynamic tensile test, and 3D reconstruction.

A novel INS/ADS integrated navigation method based on INS error model-aided unbiased converted measurement

In the inertial navigation system (INS)/ air data system (ADS) integrated navigation system, the measurements of INS and ADS need to be converted to a unified coordinate for fusion. However, the sensor noises cause serious conversion bias in the direct measurement conversion method. In this paper, a novel INS/ADS integrated navigation method based on INS error model-aided unbiased converted measurement (IEM-UCM) is proposed. First, the IEM-UCM model of ADS data from spherical frame to navigation frame is developed, which exploits the INS error model to calculate the compensation factors. Subsequently, the noise covariance of the converted measurement is derived. Finally, a standard Kalman filter (KF) is implemented for INS/ADS integrated navigation. The simulation results validate the effectiveness of the proposed method in terms of the unbiasedness and consistency of the converted measurement. The root mean square error values indicate that the proposed method outperforms the existing methods in navigation accuracy.

A novel INS/ADS integrated navigation method based on INS error model-aided unbiased converted measurement

Abstract In the inertial navigation system (INS)/ air data system (ADS) integrated navigation system, the measurements of INS and ADS need to be converted to a unified coordinate for fusion. However, the sensor noises cause serious conversion bias in the direct measurement conversion method. In this paper, a novel INS/ADS integrated navigation method based on INS error model-aided unbiased converted measurement (IEM-UCM) is proposed. First, the IEM-UCM model of ADS data from spherical frame to navigation frame is developed, which exploits the INS error model to calculate the compensation factors. Subsequently, the noise covariance of the converted measurement is derived. Finally, a standard Kalman filter (KF) is implemented for INS/ADS integrated navigation. The simulation results validate the effectiveness of the proposed method in terms of the unbiasedness and consistency of the converted measurement. The root mean square error (RMSE) values indicate that the proposed method outperforms the existing methods in navigation accuracy.

Real-time panoramic-deformation measurement of cabin structure of launch vehicle based on camera network

<p indent="0mm">To satisfy the requirement of real-time panoramic-deformation measurement of a cabin structure in a large-diameter launch vehicle, a camera-network deformation-measurement system composed of 24 cameras and real-time three-dimensional (3D) deformation-measurement software based on CPU parallel computing was developed. The 360° full-circle deformation measurement of the inner surface of a cylindrical coordinate system and real-time measurement of the local deformation of a local Cartesian coordinate system were successfully realized. The camera-network measurement system consisted of 12 sets of binocular systems. The field of view of a single set of binocular systems was 3.6 m×3.6 m, and a certain overlap area was set between two adjacent binocular systems to ensure continuity of measurements. Photogrammetry and binocular stereo vision were used to reconstruct the 3D coordinates of the coded points, and coordinate transformation between the local and global coordinate systems was realized using the coded points. Finally, coordinate unification of the 12 sets of binocular systems was performed. A single set of binocular systems could be connected to a computer. By implementing the seed-point-based parallel algorithm and initial guess-estimation-algorithm-based deformation-transfer scheme, real-time 3D displacement and surface-strain measurements at 70000 points per second were successfully realized. On the basis of the 360° full-circle deformation-measurement results in the cylindrical coordinate system, high-precision calculation of the diffusion angle was successfully realized. The standard deviation of the static noise in the three directions was less than 0.01 mm, and the standard deviation of the static noise in the strain was less than 50 microstrains.

Hypocenter Hotspots Illuminated Using a New Cross‐Correlation‐Based Hypocenter and Centroid Relocation Method

AbstractAccurate earthquake locations are important for understanding earthquake processes. Recent advances in earthquake relocation methods have resulted in relative centroid locations with smaller uncertainties using cross‐correlation‐based methods. However, relative hypocenter locations, which are traditionally determined using the onset times of event waveforms, generally possess larger uncertainties than that of the centroids. Here, we develop a new cross‐correlation‐based relative hypocenter relocation method based on the network correlation coefficient method, whereby we incorporate an additional step to search for a suitable window to cross‐correlate the event waveform onsets. We then perform a joint inversion on the obtained relative hypocenter and centroid locations to directly compare their relative locations under a unified coordinate. We apply this method to three regions where repeating earthquakes have been detected, and reveal both the repetitive rupture of similar centroids by these repeating events and newly illuminated hypocenter hotspots that serve as the onset region of earthquakes that grow into different but selective magnitudes. Our joint analysis of hypocenter and centroid locations enables quick estimations of rupture propagation directions without the need to perform slip inversion analyses. We estimate the location uncertainties via bootstrapping, with the 95% confidence intervals yielding sub‐100 m horizontal uncertainties in both the centroid and hypocenter locations with the best station coverage and event availability. These results point to limited but existing correspondence between the hypocenter and centroid locations and the earthquake magnitudes, as well as complexity in the hypocenter distributions, even within repeating earthquake sequences whose rupture areas largely overlap.

A Comparison Method for 3D Laser Point Clouds in Displacement Change Detection for Arch Dams

Dam deformation monitoring can directly identify the safe operation state of a dam in advance, which plays an important role in dam safety management. Three-dimensional (3D) terrestrial laser scanning technology is widely used in the field of deformation monitoring due to its fast, complete, and high-density 3D data acquisition capabilities. However, 3D point clouds are characterized by rough surfaces, discrete distributions, which affect the accuracy of deformation analysis of two states data. In addition, it is impossible to directly extract the correspondence points from an irregularly distributed point cloud to unify the coordinates of the two states’ data, and the correspondence lines and planes are often difficult to obtain in the natural environment. To solve the above problems, this paper studies a displacement change detection method for arch dams based on two-step point cloud registration and contour model comparison method. In the environment around a dam, the stable rock is used as the correspondence element to improve the registration accuracy, and a two-step registration method from rough to fine using the iterative closest point algorithm is present to describe the coordinate unification of the two states’ data without control network and target. Then, to analyze the displacement variation of an arch dam surface in two states and improve the accuracy of comparing the two surfaces without being affected by the roughness of the point cloud, the contour model fitting the point clouds is used to compare the change in distance between models. Finally, the method of this paper is applied to the Xiahuikeng Arch Dam, and the displacement changes of the entire dam in different periods are visualized by comparing with the existing methods. The results show that the displacement change in the middle area of the dam is generally greater than that of the two banks, increasing with the increase in elevation, which is consistent with the displacement change behavior of the arch dam during operation and can reach millimeter-level accuracy.

High-accuracy three-dimensional measurement based on multi-directional cooperative target with weighted SfM algorithm

Large-scale three-dimensional (3D) precision measurement is limited by the high-accuracy single-view measurement range. Therefore, it is necessary to perform multi-station coordinate transformation through high-precision 3D cooperative target to complete the measurement for large-scale objects. However, high-precision 3D cooperative target is difficult to be manufactured. The physical features on the cooperative target are also easy to occlude each other in complex measurement environments. To solve these problems and reduce the number of coordinate transformations in the process of global coordinate unification, a Multi-directional Cooperative (MDC) target consisting of designed coded planar feature target and polyhedron skeletons is designed. The designed MDC target is arbitrarily placed in the Field of View (FOV) and images of the target are obtained from multiple views. Based on the captured target images, a new Weighted SfM algorithm is proposed to realize the high-accuracy 3D measurement for the MDC target. The experimental results show that the proposed algorithm has good robustness and high accuracy for the designed MDC target.

Projection aided digital shearography scanning detection technology

Digital shearography has been widely used in non-destructive testing of composite materials. But the conventional detection area is limited and the defect detection of large-size composite materials is difficult to achieve. A digital shearography assisted with projection scanning detection for large-size composite materials was introduced. Based on digital shearography and the auxiliary projection which could project additional surface features, global matching and coordinate unification of the sub-view original images and the speckle interferograms were calculated by the homography matrix of the corresponding projection images to realize the multi-field scanning and matching stitching of projection, origin, and interferogram images. At the same time, a 4f optical path and an ultra-wide-angle lens were imported for the one detection to expand the detection area. Experimental results show that the scanning detection system can achieve more accurate measurement of the defect position and size. The root mean square error of location positioning is 7.0 mm, and the root mean square value of dimension measurement error is 4.9 mm. At a working distance of 1.2 m, the single detection area can reach 600 mm×500 mm and the global scanning detection area can reach 3.5 m×4.0 m. This method has the advantages of strong resistance to rigid body displacement interference and high sensitivity of defect detection, which is suitable for non-destructive testing of large-size and high performance composite materials.

Collision risk evaluation method for flight trajectory with uncertainty

With the development of intelligent and unmanned transportation, the automation management of the transportation system has become an inevitable trend, and collision risk is of paramount consideration in traffic management automation. In this paper, we propose a collision risk evaluation method, which takes collision probability as a safety-evaluation index, and hence, can control collision risks. We developed the proposed methodology in a simple form that consists of three steps for data preprocessing, spatiotemporal discretization, and probability calculation. First, we perform coordinate unification and time-alignment for the given trajectory data. Then, we divide the trajectory into many small segments according to the interpolation interval and divide the uncertainty cuboid into many grid cells according to the given safe distance. Finally, we calculate the instantaneous collision probability according to the probability distribution model, and then, we obtain the collision probability of the entire flight period. Simulation results reveal that the method has convergence for the interpolation interval. We also discuss the effects of safe distance on the calculation result of collision probability. The proposed method has a wide range of application scenarios, which can be applied to traffic management decision support tools of various types of vehicles as long as an appropriate safe distance is adopted.

An Automatic Coordinate Unification Method of Multitemporal Point Clouds Based on Virtual Reference Datum Detection

Deformation monitoring of structures is a common application and one of the major tasks in surveying engineering, but there is a great challenge for the coordinate datum unification of multitemporal point cloud data. To address this problem, an automatic detection method of the virtual reference datum in multitemporal point cloud is proposed in this article. To obtain the corresponding grids of multitemporal point cloud, an appropriate coarse registration algorithm is adopted to approximately transform the multitemporal data into a unified coordinate system. Then, the stable areas are extracted based on the probability density functions similarity of the corresponding grids, which are defined as the virtual reference datum. Furthermore, an improved 3D normal distribution transform algorithm considering the cell boundaries and iteratively selecting an appropriate cell size is constructed to achieve fine registration of the virtual reference datum. Finally, the coordinate unification of the multitemporal point cloud is implemented according to the transformation parameters of the virtual reference datum. The proposed method is tested on a landslide point cloud, which is captured by static terrestrial laser scanning. The virtual reference datum extraction accuracy of the two-temporal landslide point cloud captured on the same day is 96%, and the coordinate unification accuracy is 3 mm. The experimental results demonstrate that the proposed method is effective in coordinate datum unification of multitemporal point cloud.

Spatio-Temporal Alignment and Trajectory Matching for Netted Radar Without Prior Spatial Information and Time Delay

For netted radars, a new alignment algorithm without prior spatial information (i.e., locations and attitudes of radars) and time delay is proposed to keep the spatio-temporal alignment. The unknown parameters to be estimated include the rotation matrix, the translation vector and the delay between radar stations in this paper. The minimum error function in unified space and time coordinate system is established based on the target trajectory measured by each radar. Firstly, the alternating spatio-temporal alignment method is used to estimate the spatial and temporal parameters, and its statistical performance is compared to the Cramer-Rao bound. Even under severe conditions in which each radar in the network can only observe part of the trajectory, the proposed algorithm can still be adopted to estimate the alignment parameters and complete the trajectory. Then, a trajectory matching algorithm based on random sample consensus (RANSAC) is proposed for multitarget. The corresponding relationship between trajectories is established through minimizing sum of paired trajectory error, and multitarget trajectories from each radar are matched. Finally, the spatio-temporal parameters are refined by all matched trajectory pairs. Simulation results show that the tracking information from different radars is transformed into a unified coordinate after spatio-temporal alignment. Moreover, the registration error is reduced and the tracking accuracy is improved.

Girth Measurement Based on Multi-View Stereo Images for Garment Design

In this paper, we propose a novel girth measurement system based on multi-view stereo images for garment design. Our system is set in a fixed location to capture three pairs of stereo images for the subject by six calibrated and synchronously triggered cameras. An important feature of this system is the use of an optimized semantic segmentation network that can efficiently segment the girth region in the captured six-view stereo images. Another important feature of this system is the use of color subspace classification and coordinate clustering that can effectively constrain the stereo matching within the scope of markers. Then, the system performs only on the corresponding clusters to extract stereo matching point pairs of markers correctly. The space coordinates of 3D point corresponding to each stereo matching point pair are calculated in each coordinate system of stereo cameras. The unified coordinates of these 3D markers are transformed from three different coordinate systems into one unified coordinate system. Girth is measured by curve fitting of these markers and calculating the length of the fitting curve. Our proposed system performs passive and intelligent girth measurement in garment design, and overcomes the problem of too many invalid stereo matching point pairs in girth measurement. Experimental results demonstrate its accuracy. Our system has a maximum bust measurement error of 1.28cm for woman and 1.31cm for man and a maximum waist measurement error of 1.18cm for woman and 0.99cm for man, which are within the error limit regulated by China national standards GB/T 2664-2017, 2665-2017, 2666-2017 and textile industry standard FZ/T 81004-2012.

An Emergency Seamless Positioning Technique Based on ad hoc UWB Networking Using Robust EKF.

In this paper, a new emergency positioning technique is proposed based on ad hoc GNSS/UWB (Global Navigation Satellite System/Ultra-Wideband) network. The main innovations of the program are reflected in two aspects. First of all, a unified coordinate frame for indoor and outdoor environments is constructed dynamically with GNSS/UWB integration. In the outdoor environments, the high accuracy positioning can be achieved with GNSS/UWB equipment. The high-accuracy indoor coordinate is obtained by measuring the range observations between adjacent network nodes and outdoor GNSS/UWB nodes, and the range information of the UWB network is transmitted to the cloud server center. A network adjustment algorithm is proposed to improve the positioning accuracy of the UWB network. Secondly, a UWB indoor location algorithm based on robust EKF (Extended Kalman Filter) is proposed. By analyzing the transfer characteristics of gross error in EKF model, a new robust EKF model is established. The model is constructed based on the statistical characteristics of redundant observation components and prediction residual. The robust equivalent gain matrix is constructed, and the robust positioning solution of UWB is obtained with iteration. The global test is carried out first to further improve the real-time operation efficiency. Finally, a field indoor and outdoor seamless positioning experiment was carried out to verify the effectiveness of the proposed algorithm. The results show that the positioning accuracy of UWB emergency network nodes (anchors) can reach 0.35 m. Based on the network, the positioning accuracy of the tag can reach 0.38 m by applying the improved robust EKF positioning algorithm, which is improved by 20.83% and 73.43% compared with standard EKF and least square method, respectively.

A unification method of locally identified linear systems and reconstruction of nonlinear dynamical model

In this study, we propose an identification method of a state-space model for discrete-time SISO nonlinear systems. The first step of the procedure is to construct models which approximate local dynamics around stationary points of the system using a subspace method. The second step is to describe the local models in one unified coordinate using affine coordinate transformations. The coordinate transformations are determined based on the similarity of adjacent local models by the least-squares method. The model describes quasi-steady behavior of the plant. Simulation results of the proposed method are presented.

Multi-scale referencing and coordinate unification of optical sensors in multi-axis machines

Abstract Multi-scale optical sensor systems help to overcome the area of conflict between resolution, field size, and inspection time if it comes to the frequent problem of detecting small defects on large areas. The sensors of such systems are chosen according to two main properties: On the one hand, they should measure in opposed scales; on the other hand, their measurement principles should vary as well in order to be suitable for different material and surface properties. However, these systems can only operate at full capacity if it is possible to unify the acquired data from each sensor into one common coordinate system such that an overall analysis is possible, or subsequent sub-measurements can be triggered. In this paper, a general approach for a common sensor referencing is proposed, whose focus lies on microscopic optical sensors for both scattering and reflecting surfaces. The method is able to handle resolutions from the nanometer to millimeter scale in one single system, but is also feasible for a coordinate unification across several single sensor systems.

Multi-camera global calibration for large-scale measurement based on plane mirror

After analyzing and investigating the optical imaging geometry, a new multi-camera calibration method based on the planar mirror is proposed, combined with intermediary coordinates uniform method. By this method, the coordinate unification is achieved for two binocular vision pairs without overlapping view, which finally leads to the global unification of the whole multi-camera system. A multi-camera vision system is set up, including four CCDs, one mirror, and a plane template. Through global calibration by this new method, a displacement of 1100mm by a high-accuracy motor-driven moving platform is measured, with the average relative error 0.0205%, and the standard deviation 0.0740mm. The results show that the calibration method is simple and has high accuracy, and is suitable for precision measurement of large-scale measurands.