Position And Orientation System Research Articles

Exterior Orientation Parameter Refinement of the First Chinese Airborne Three-Line Scanner Mapping System AMS-3000

The exterior orientation parameters (EOPs) provided by the self-developed position and orientation system (POS) of the first Chinese airborne three-line scanner mapping system, AMS-3000, are impacted by jitter, resulting in waveform distortions in rectified images. This study introduces a Gaussian Markov EOP refinement method enhanced by cubic spline interpolation to mitigate stochastic jitter errors. Our method first projects tri-view images onto a mean elevation plane using POS-provided EOPs to generate Level 1 images for dense matching. Matched points are then back-projected to the original Level 0 images for the bundle adjustment based on the Gaussian Markov model. Finally, cubic spline interpolation is employed to obtain EOPs for lines without observations. Experimental comparisons with the piecewise polynomial model (PPM) and Lagrange interpolation model (LIM) demonstrate that our method outperformed these models in terms of geo-referencing accuracy, EOP refinement metric, and visual performance. Specifically, the line fitting accuracies of four linear features on Level 1 images were evaluated to assess EOP refinement performance. The refinement performance of our method showed improvements of 50%, 45.1%, 29.9%, and 44.6% over the LIM, and 12.9%, 69.2%, 69.6%, and 49.3% over the PPM. Additionally, our method exhibited the best visual performance on these linear features.

A 3D imaging method for flexible array SAR based on a distributed POS

ABSTRACT The longer the baseline length of an airborne array synthetic aperture radar (SAR) is, the richer the ground object information can be resolved. The airborne array flexible SAR can overcome the limitation that the baseline length is limited by the belly length by attaching the antenna to the wing. However, fusing the airborne array flexible SAR distributed position and orientation system (POS) data under the wing and the belly is a major difficulty. To address this issue, the paper analyzes the inconsistency of airborne array flexible SAR data with a distributed POS and proposes a three-dimensional imaging method of airborne array flexible SAR based on distributed POS by fusing the Doppler centre, yaw angle and time of the observation data. By using distributed POS data, flexible data can obtain higher resolution than single-POS processing. The experiment of airborne array flexible SAR data proves the superiority of the proposed method.

Transfer alignment method based on installation deviation angle estimation for array POS

Transfer alignment of array Position and Orientation System (POS) is the key technology to provide motion information for airborne earth observation high-precision imaging. Because the large deviation angle between main Inertial Measurement Unit (IMU) and slave IMU is difficult to accurately measure by existing methods, the transfer alignment accuracy will be seriously affected. Aiming at this issue, a transfer alignment method based on installation deviation angle estimation for array POS is proposed. This method includes two steps: pre-estimation and improved transfer alignment. First, two deviation angles are calculated by angular velocity and acceleration using quaternion optimization method respectively. The higher-precision deviation angle is fused by two deviation angles using their respective weights, which are calculated by Signal to Noise Ratios (SNRs). Second, the result of pre-estimation is used in transfer alignment. Residual errors of deviation angle are considered as state variables and be compensated online. Compared with the existing methods, the velocity, position and attitude errors calculated by this method are reduced by 15.4%, 31.7% and 66.5% respectively. The proposed method significantly reduces the adverse effect of installation deviation angle on measurement accuracy of array POS.

Unmanned Airborne Bistatic Interferometric Synthetic Aperture Radar Data Processing Method Using Bi-Directional Synchronization Chain Signals

The bistatic Interferometric Synthetic Aperture Radar (InSAR) system can overcome the physical limitations imposed by the baseline of monostatic dual-antenna InSAR. It provides greater flexibility and can enhance elevation measurement accuracy through a well-designed baseline configuration. Unmanned aerial vehicles (UAVs) equipped with bistatic InSAR, having relatively low cost and high flexibility, are useful for mapping and land resource exploration. However, due to challenges including spatiotemporal synchronization and motion errors, there are limited reports on UAV-borne bistatic InSAR. This paper proposes a comprehensive method for processing data from small UAV-borne bistatic InSAR by integrating two-way synchronization chain signals. The proposed method includes compensation for time and phase synchronization errors, trajectory refinement with synchronized chain and Position and Orientation System (POS) data, high-precision bistatic InSAR imaging, and interferometric processing. Height inversion results based on the proposed method are also provided, which demonstrate the effectiveness of the proposed method in improving the accuracy of interferometric measurement at calibration points from 0.66 m to 0.42 m.

An incremental LiDAR/POS online calibration method

Extrinsic Calibration between LiDAR and POS (position and orientation system) is a fundamental prerequisite for varieties of MLS (mobile laser scanner) applications. Due to the sparse structure of LiDAR data, the current calibration methods relying on common point feature matching are unreliable, and the low accuracy POS results make the extrinsic calibration of MLS system more challenging. In this paper, we propose an incremental estimation method of six degree of freedom extrinsic transformation of LiDAR and POS. Firstly, the POS-SLAM is used to accumulate LiDAR scans as online sub maps. Attitudes of the carrier are calculated by using GNSS/INS loose combination method of bidirectional adjustment, and scans are associated with sub map based on the time interpolation. Then, the extrinsic calibration parameters are estimated by optimizing corresponding points difference between SLAM and MLS coordinate frame. Finally, field tests have been conducted to the proposed method. RMS between the map by the calibrated MLS and by the static measurement is 0.57 cm. The results demonstrate that the accuracy and robustness of our calibration approach are sufficient for mapping requirement of MLS.

A modeling and calibrating method of FBG sensors for wing deformation displacement measurement

The airborne distributed Position and Orientation System (POS) is a key piece of equipment for providing high-precision motion parameters for aerial remote sensing systems. However, wing deformation degrades the performance of distributed POS, thus, it is urgent to obtain high-precision deformation information to assist distributed POS. In this study, a modeling and calibrating method of fiber Bragg grating (FBG) sensors for wing deformation displacement measurement is proposed. First, based on the cantilever beam theory and piecewise superposition, a modeling and calibrating method for wing deformation displacement measurement is established. The wing is then placed under different deformation conditions, and the changes in the wing deformation displacement and corresponding wavelength variations of the pasted FBG sensors are obtained using theodolite coordinate measurement system and FBG demodulator, respectively. Subsequently, linear least square fitting is deployed to develop the relationship model between the wavelength variations of the FBG sensors and wing deformation displacement. Finally, the wing deformation displacement at the measuring point in the temporal and spatial dimensions is obtained by fitting and interpolation. An experiment is conducted, and the results show that the accuracy of the proposed method can reach 0.721 mm with a wing length of 3 m, which can be used in the motion compensation of an airborne distributed POS.

UAV Abnormal State Detection Model Based on Timestamp Slice and Multi-Separable CNN

With the rapid development of UAVs (Unmanned Aerial Vehicles), abnormal state detection has become a critical technology to ensure the flight safety of UAVs. The position and orientation system (POS) data, etc., used to evaluate UAV flight status are from different sensors. The traditional abnormal state detection model ignores the difference of POS data in the frequency domain during feature learning, which leads to the loss of key feature information and limits the further improvement of detection performance. To deal with this and improve UAV flight safety, this paper presents a method for detecting the abnormal state of a UAV based on a timestamp slice and multi-separable convolutional neural network (TS-MSCNN). Firstly, TS-MSCNN divides the POS data reasonably in the time domain by setting a set of specific timestamps and then extracts and fuses the key features to avoid the loss of feature information. Secondly, TS-MSCNN converts these feature data into grayscale images by data reconstruction. Lastly, TS-MSCNN utilizes a multi-separable convolution neural network (MSCNN) to learn key features more effectively. The binary and multi-classification experiments conducted on the real flight data, Air Lab Fault and Anomaly (ALFA), demonstrate that the TS-MSCNN outperforms traditional machine learning (ML) and the latest deep learning methods in terms of accuracy.

The Impact of Systematic Attitude Error on the Measurement of Interferometric Radar Altimeter

Altimetric error has always been the significant performance parameter of the interferometric radar altimeter (IRA), particularly in the observation of sub-mesoscale ocean dynamic processes in which a higher accuracy of sea surface elevation (SSE) measurement is needed. The systematic attitude error of IRA associated with platform altitude, roll, pitch, and yaw errors is a remarkable source of altimetric error. However, the coupling attitude altimetric error is still less discussed up to now. In this paper, we focus on the study of the coupling attitude altimetric error and its related position-shifting, which are all induced by the attitude errors. The theoretical formulas of the coupling attitude altimetric error were derived, and the theoretical analysis demonstrates that the coupling attitude altimetric error is no longer along the range direction of the IRA image rigorously due to the change of the radar beam pointing. Based on theoretical formulas proposed by this study, the coupling attitude altimetric error and its related position-shifting are simulated and verified by using attitude data recorded by an airborne position and orientation system (POS) of three airborne experiments. The experimental results illustrate that the simulated coupling attitude altimetric errors are consistent with the measurements of the airborne experiments.

An Improved ASIFT Image Feature Matching Algorithm Based on POS Information.

The affine scale-invariant feature transform (ASIFT) algorithm is a feature extraction algorithm with affinity and scale invariance, which is suitable for image feature matching using unmanned aerial vehicles (UAVs). However, there are many problems in the matching process, such as the low efficiency and mismatching. In order to improve the matching efficiency, this algorithm firstly simulates image distortion based on the position and orientation system (POS) information from real-time UAV measurements to reduce the number of simulated images. Then, the scale-invariant feature transform (SIFT) algorithm is used for feature point detection, and the extracted feature points are combined with the binary robust invariant scalable keypoints (BRISK) descriptor to generate the binary feature descriptor, which is matched using the Hamming distance. Finally, in order to improve the matching accuracy of the UAV images, based on the random sample consensus (RANSAC) a false matching eliminated algorithm is proposed. Through four groups of experiments, the proposed algorithm is compared with the SIFT and ASIFT. The results show that the algorithm can optimize the matching effect and improve the matching speed.

A novel relative motion measurement method based on distributed POS relative parameters matching transfer alignment

The relative motion measurement has become the key technology for high precision imaging of array Synthetic Aperture Radar (SAR), which can be obtained by distributed Position and Orientation System (POS). The traditional method based on absolute parameters matching transfer alignment can measure the relative motion, but it will be affected by the main-system motion error and reduce precision. To solve this problem, a novel relative motion measurement method based on distributed POS relative parameters matching transfer alignment is proposed. Firstly, the relative inertial navigation algorithm is used to preliminarily measure the relative motion. Then, the transfer alignment model based on relative parameters matching is established to rectify the inertial measured error and obtain the high-precision relative motion. Finally, experimental results show the proposed method is effective to isolate the main-system motion error and improve relative motion measurement accuracy compared to the traditional method.

Design and Development of Array POS for Airborne Remote Sensing Motion Compensation

Multi-antenna airborne remote sensing systems have received more attention recently because they can realize high-resolution three-dimensional (3-D) imaging, such as array Synthetic Aperture Radar (SAR). Their high-precision imaging needs multi-antenna motion and relative motion between antennas. However, the existing facility and technology hardly meet the motion measurement precision demand of array SAR. To solve this problem, an array Position and Orientation System (POS) for airborne remote sensing motion compensation is designed and developed. It is composed of a high-precision POS, several small-size Inertial Measurement Units (IMU), and a 6-D deformation measurement system based on Fiber Bragg Grating (FBG) sensors. Firstly, the transfer alignment method based on 6-D deformation is used to measure the relative motion between array POS. Then, the motion conversion method from array POS to array SAR is presented to obtain the multi-antenna motion and relative motion between antennas. Finally, the ground experiment results identify that the accuracies of multi-antenna position, multi-antenna attitude, and flexible baseline length between antennas are superior to 3 cm, 0.01°, and 0.1 mm, respectively, which can meet the motion measurement precision demand of array SAR.

A pedestrian POS for indoor Mobile Mapping System based on foot-mounted visual–inertial sensors

The mobile mapping system (MMS) is an enabling technology for indoor location-based services. The position and orientation system (POS) is one of the cores of the mobile mapping system. This paper proposes a pedestrian POS solution for infrastructure-free environments based on the fusion of foot-mounted IMU and stereo camera. The structure from motion (SFM) constructs the trajectory and environment under the condition with rich visual textures; the stereo camera detects loop-closure for mitigating the accumulated error; the foot-mounted pedestrian dead reckoning (Foot-PDR) provides reliable continuous trajectories using the IMU when the visual-based system degrades or crashes. The feasibility and reliability of the proposed system were verified by an office building test and an underground parking lot test. The proposed system achieved 0.237 m and 0.227 m position errors in these two scenarios, respectively. Moreover, the system can maintain the average position accuracy of 0.3 m when the camera shortly failed in some areas.

Comparative Analysis of Single-View and Multi-View Airborne SAR Positioning Error and Course Planning for Multi-View Airborne SAR Optimal Positioning

Airborne synthetic aperture radar (Airborne SAR) can accurately locate key targets and regions by using the flight parameters of the aircraft and the relative position information between the aircraft and the target which can be obtained from the airborne positioning and orientation system (POS). In the course of flight, the aircraft will deviate from the ideal flight path due to atmospheric turbulence, which results in the calculation deviating from the actual target position. In order to improve the target positioning accuracy, it is necessary to study the influence of aircraft motion error on the target positioning error. This study discusses the positioning accuracy of single-view airborne SAR from the perspective of linear Range-Doppler algorithm (RDA), and deduces the multi-view airborne SAR positioning error transfer model based on the multi-view airborne SAR positioning model. Based on these, we analyze the main factors that affect the positioning accuracy of the two positioning methods in detail and quantitatively reveal the mechanism by which the multi-view airborne SAR positioning method can improve the target positioning accuracy compared with the single-view airborne SAR positioning method; we also solve the problem of course planning for multi-view airborne SAR optimal positioning. The research results can provide theoretical support for the analysis of factors influencing positioning error and the positioning error correction of airborne SAR.

PERFORMANCE ANALYSIS OF SEMANTIC REFRESH INDOOR NAVIGATION FOR SMARTPHONE’S SENSORS USING INS/VINS INTEGRATION SCHEME

Abstract. Positioning and Orientation System (POS), which integrates Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS), is widely used to accomplish outdoor navigation missions for land vehicles. However, the positioning accuracy would become worse in GNSS-hostile environments (Chiang et al., 2013), which is quite challenging to accomplish indoor navigation environments. Nevertheless, smartphones are contained many embedded sensors, including GNSS, IMU, camera, which have the potential to be an ideal personal navigation device. In this research, we mainly propose an integrated scheme of INS/VINS/object detection refresh (ODR) for indoor challenging environments. The goal is to achieve indoor navigation for vehicular applications only using smartphones. The algorithm is developed based on the smartphone. By the conventional inertial navigation system, which is integrated with two designed processes to further improve the performance. First is assistance from the visual-inertial navigation system (VINS). The long-term drift caused by the INS could be decreased effectively, and complete the extended Kalman filter (EKF) composition. The second is to apply neural network, YOLO-v3 (Redmon et al.,2018), to detect objects and provide the object's describer information to refresh the proper position. Therefore, the proposed method uses visual estimation and recognition methods to assist the smartphone platform to obtain a more accurate solution.Finally, we use the navigation-grade IMU as the reference system for accuracy verification. The accuracy comparisons of the three integration solutions are analysed reasonably. The position accuracy is reasonable. Compared with the original smartphone INS integration method, the proposed integration scheme improves the accuracy from the horizontal direction by 78.5%.

Case Study: Rigorous Boresight Alignment of a Marine Mobile LiDAR System Addressing the Specific Demands of Port Infrastructure Monitoring

Accurate infrastructure monitoring of ports and harbors is a vital operation conducted by the port’s authority. To operate regularly in this highly dynamic environment, we explore the potential of the cutting-edge mobile LiDAR systems (MLS) mounted on a vessel. To generate a high-quality 3 D point cloud that would satisfy the expected accuracy required in the monitoring task, the LiDAR scanner and the positioning and orientation system (POS) must be angularly aligned also known as boresight alignment. In this research, we introduce a boresight alignment methodology adapted to the port infrastructure surveillance based on prefabricated planar targets. After an analysis of planar target simulated data, we propose a boresight alignment site design. Then, we apply this boresight alignment site design in a real-world scenario. Obtained results estimate accurately roll and yaw angles errors with standard deviations of less than 0.002 degrees and pitch angle error with standard deviation less than 0.015 degrees. Finally, we defined a validation site and described the procedure that uses these features to validate the quality of the estimated parameters. The relative comparison of the georeferenced point clouds, before and after boresight alignment demonstrates the mitigation of the boresight systematic error impact on the final point cloud.

Enhanced Disturbance Suppression Method Based on NonlinearH∞Filtering for Distributed POS in Aerial Earth Observation Imaging Application

Multinode remote sensing imaging loads have long been a fascinating focus of aerial earth observation applications, where the load denotes the device or equipment located on the aircraft for a specific task, such as aerial camera, scanning laser, and synthetic aperture radar. In order to guarantee high-quality imaging, distributed position and orientation system (POS) serves as high accuracy spatio-temporal reference information for remote sensing loads to realize motion compensation. As a result of the working environment, the flexible structure of the wing will suffer the external disturbance, then for distributed POS, the lever arm between the master system below the abdomen and slave system under the wing is time-varying, also the noise disturbance influences markedly on the motion parameters measurement accuracy. Then aiming at the transfer alignment accuracy improvement of distributed POS, a central difference <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> filtering algorithm is proposed, which adopts the central difference Kalman filtering to realize nonlinear state estimation and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> filtering to suppress the disturbance influence on estimation, then the high accuracy motion parameters can be obtained. The flight test results show that the developed algorithm can effectively deal with the disturbance influence on transfer alignment estimation and enhance the parameter estimation accuracy, which can be used in aerial earth observation imaging.

An Intensity-Independent Stereo Registration Method of Push-Broom Hyperspectral Scanner and LiDAR on UAV Platforms

Unmanned aerial vehicles (UAVs) equipped with hyperspectral scanners and LiDARs can flexibly acquire rich spectral and geometric information about the observation scene. To combine the complementary advantages of multi-source data, the stereo registration of hyperspectral images and LiDAR data has become one of the hot topics in remote sensing community. However, existing research works are more focused on exploiting intensity information from multi-source data, which is applicable to data acquired on manned vehicle platforms or satellite platforms. For UAV platforms with poor stability and limited load, the low signal-to-noise ratio of LiDAR data and the complex distortion of push-broom images bring great challenges to stereo registration. Under this circumstance, an intensity-independent stereo registration method is proposed in this paper, which is based on the physical model of the integrated system and the sensor detection principles. Specifically, the proposed method utilizes the position and orientation system (POS) to reduce the impact of UAV platform motion on hyperspectral imaging, and projection errors are eliminated by the ray tracing model with aid of LiDAR data. Finally, a virtual ray decomposition model based on geometric features is constructed to realize the stereo registration of hyperspectral images and LiDAR data. Compared with an advanced solution and professional processing software, the proposed method has shown better registration performance on two data of different scenarios.

A Motion Information Acquisition Algorithm of Multiantenna SAR Installed on Flexible and Discontinuous Structure Based on Distributed POS

Airborne multi-antenna SAR urgently needs the high-precision motion information of each antenna to realize high-precision imaging. Distributed Position and Orientation System (POS) is an important way to solve this problem based on transfer alignment. Because multiple antennas are usually installed on the flexible and discontinuous structure, there are complex and time-varying relative motions between different antennas. This relative motion will lead to the decline of transfer alignment accuracy, and directly affects the imaging accuracy of SAR. To solve this problem, a motion information acquisition algorithm of multi-antenna SAR installed on flexible and discontinuous structure based on distributed POS is given. Firstly, the gyroscopes and accelerometers of the distributed POS are used to calculate the relative attitude between nodes, which is applicable to both continuous and discontinuous structure. Secondly, combining the smooth estimation and the distributed filter, a distributed smoothing algorithm including forward filter and backward smoothing is proposed to calculate the high-precision and smoothed motion data of each sub-node. The forward filter obtains the motion parameters of each sub-node through transfer alignment based on the above relative attitude data. The backward smoothing further improves the motion information accuracy of sub-node by using the measurement data at all time and sharing the data of all nodes. Through the flight imaging test of multi-antenna SAR installed on a flexible and discontinuous platform, it is shown that the proposed algorithm can significantly improve the accuracy of the sub-node motion data, in which the errors of position, velocity and attitude are reduced by 58.9%, 61.9% and 71.2% respectively. Based on this motion data, SAR achieves better imaging focusing effect.

A hierarchical approach for refining point cloud quality of a low cost UAV LiDAR system in the urban environment

Insufficient accuracies of the low end position and orientation system (POS) used in low cost UAV LiDAR systems (ULSs) cause the direct georeferencing data to lead to poor point cloud quality. Trajectory correction and scan-to-map matching are two commonly used strategies for point cloud quality refinement. The existing trajectory correction strategies work with the assumption that POS errors can be modeled as a time-variant function, which cannot be applied to the low end POS. The existing scan-to-map matching methods have difficulty refining the ULS point clouds due to the large gaps between scan lines. This paper proposes HR-ULS, hierarchical refinement for low cost ULS point cloud quality in the urban environment, to solve these challenges. HR-ULS separated the raw laser scanning point clouds into a set of scan-blocks and refined the point cloud quality with a hierarchical strategy, resulting in local and global optimization, respectively. First, the internal scan-block matching (ISBM) estimated multiscale distributions for each laser frame and calculated relative motions iteratively to achieve local map consistency in each scan-block. Second, the multiview scan-block matching (MSBM) took inertial, Global Navigation Satellite System (GNSS), and laser measurements into a unified adjustment framework to correct the trajectory, achieving global map consistency between scan-blocks. Comprehensive experiments evaluated the proposed HR-ULS with the point clouds captured by a low-cost ULS in three typical urban areas. They showed that the average plane fitting RMSE of the ULS point clouds was improved from 0.34 m to 0.09 m, and the average checkpoint offset was improved from 1.86 m to 0.21 m, achieving an identical level of accuracy with that of direct georeferencing using a high end POS, APX-15-UAV.

Application of Transformed Cubature Quadrature Information Filtering in Distributed POS

For aerial earth observation system, the accuracy of motion parameters provided by airborne distributed position and orientation system (POS) is crucial to the observation load imaging quality. According to the working principle, slave systems of distributed POS rely on the master node information to realize error estimation and correction, and then to achieve multiple node high accuracy measurement. Due to the nonlinearity of the error model, nonlinear filtering algorithm is used to the estimation process. Then in order to achieve higher estimation performance, a transformed cubature quadrature information filtering algorithm is developed for distributed POS estimation. In this method, the contributions are located that: (1) spherical radial cubature rule and Gauss-Laguerre quadrature rule are used to generate sigma points; (2) orthogonal transformation is conducted on the sampling points to deal with nonlocal sampling; (3) information filtering framework is adopted to simplify filtering update process, then the algorithm is applied in distributed POS nonlinear transfer alignment estimation. By the flight test validation, the outcomes are found that the developed algorithm can enhance the distributed POS estimation accuracy effectively, including attitude, velocity, position accuracy, then can be used to loads imaging quality improvement.