Relative Pose Measurements Research Articles

Accurate relative measurement of multitarget poses by monocular vision for nonmodel-based real-time calibration of industrial robot

On basis of multitarget poses measured by monocular vision, a nonmodel-based real-time calibration method for six-DoF industrial robot is presented. Markers fixed on base and joints of robot are measured by a camera synchronously, at each measurement configuration selected with constraints of marker visibility and work trajectory, to provide decoupled pose data. The accuracy problem due to low precision of camera is addressed by substituting absolute measurement of movable markers with relative measurement from a static marker to the movable ones. On this basis, the deviated pose of each joint can be obtained accurately and the actual kinematic parameters of robot are obtained. The simulation verifies the selection algorithm of measurement configurations and evaluates the effects of decisive error sources on measurement accuracy. The experiment demonstrates the advantage of relative pose measurement using the low-cost camera on calibration accuracy by comparison with typical model-based calibrations using a precise laser tracker.

High-precision method for simultaneously measuring the six-degree-of-freedom relative position and pose deformation of satellites.

The high-precision measurement of the six degrees-of-freedom (6DoF) relative position and pose deformation of satellites on the ground in vacuum and high-/low-temperature environments plays a critical role in ensuring the on-orbit mapping accuracy of satellites. To meet the strict measurement requirements for a satellite of a high accuracy, high stability, and a miniaturized measurement system, this paper proposes a laser measurement method for simultaneously measuring 6DoF relative position and attitude. In particular, a miniaturized measurement system was developed and a measurement model was established. The problem of error crosstalk between the 6DoF relative position and pose measurements was solved by conducting a theoretical analysis and OpticStudio software simulation, and the measurement accuracy was improved. Laboratory experiments and field tests were then conducted. The experimental results revealed that the measurement accuracy of the developed system for the relative position and relative attitude reached 0.2 µm and 0.4", within the measurement ranges of 500 mm along the X axis, ±100 µm along Y and Z axes, and ±100", and the 24-h measurement stabilities were superior to 0.5 µm and 0.5", respectively, which meets the ground measurement requirements for the satellite. The developed system was successfully applied on site, and the 6Dof relative position and pose deformation of the satellite were obtained via a thermal load test. This novel measurement method and system provides an experimental means for satellite development, in addition to a method for the high-precision measurement of the relative 6DoF position and pose between two points.

Design and Experiment of Spacecraft Relative Motion Simulation and Relative Pose Measurement Evaluation System

Design and Experiment of Spacecraft Relative Motion Simulation and Relative Pose Measurement Evaluation System

High-Accuracy Relative Pose Measurement of Noncooperative Objects Based on Double-Constrained Intersurface Mutual Projections.

Relative pose measurement for noncooperative objects is an important part of 3D shape recognition and motion tracking. The methods based on scanning point clouds have better environmental adaptability and stability than image-based methods. However, the discrete points obtained from a continuous surface are sparse, which leads to point-to-point dislocations in the overlapping area and seriously reduces the accuracy. Therefore, this paper proposed a relative-pose-measurement algorithm based on double-constrained intersurface mutual projections. First, the initial corresponding set was constructed using mutual projections between the areas with similar feature descriptors, and then the final corresponding set was determined through the rigid-transformation-consistency constraint to improve the accuracy of the matchings and achieve a high-accuracy relative pose measurement. In the Stanford dataset, the rotation error and translation error were reduced by 19.3% and 13.4%, respectively. Furthermore, based on the proposed evaluation method, which separated the error of the pose-measurement algorithm from that of the instrument, the experiments were carried out with a self-made swept-frequency interferometer. The rotation error was reduced by 39.8%, and the surface deviation was reduced by 4.9%, which further proved the advancement of the method.

Relative Stereovision-Based Navigation for Noncooperative Spacecraft via Feature Extraction

Spacecraft proximity operations play an important role in guaranteeing the capture success of noncooperative targets. This article technically presents a relative stereovision-based navigation scheme that ensures accurate relative pose measurement as well as practical motion estimation for proximity operations. Specifically, features parameters of the line and circle on the noncooperative target are first obtained by the different image processing extraction algorithms. Then, the center position and normal of the circle are derived by orthogonal transformation from the circle feature parameters, and two features descriptors are defined by the redundant parameters in the stereovision to evaluate error levels of the extraction algorithms. In particular, the unavailable roll angle around the circle normal is recovered by incorporating the line and the descriptors by utilizing the least squares method, wherein the relative attitude is proved less affected by the features extraction errors, and thus, the measurement accuracy is theoretically improved. Furthermore, considering the Mahalanobis distances of the attitude and position, two outlier detectors are coupled with the 3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma$</tex-math></inline-formula> rule in a multiplicative extended Kalman filter to achieve outlier rejection and motion estimation, respectively. Finally, numerical simulation and physical experiment are given to validate the effectiveness of the proposed approach.

Cooperative Localization Using the 3D Euler–Lagrange Vehicle Model

Kalman filter-based cooperative localization (CL) algorithms have been shown to significantly improve pose estimations within networks of vehicles but have relied predominantly on two-dimensional kinematic models of the member agents. An inherent deficiency of the commonly employed kinematic vehicle model is the ineffectiveness of CL with only relative position measurements. In this work, we present a singularity-free CL using the full three-dimensional (3D) nonlinear dynamic vehicle model suitable for decentralized control and navigation of heterogeneous networks. We develop the algorithm, present Monte Carlo simulation results with relative pose measurements, and assess the algorithm performance as the number of measurements increases. We further demonstrate that CL with only relative position measurements is effective when using the dynamic model and benefits from increasing number of measurements. We also evaluate the performance of CL with respect to measurement task distribution, which is important in cooperative control of autonomous vehicles.

Fast Loop Closure Selection Method with Spatiotemporal Consistency for Multi-Robot Map Fusion

This paper presents a robust method based on graph topology to find the topologically correct and consistent subset of inter-robot relative pose measurements for multi-robot map fusion. However, the absence of good prior on relative pose gives a severe challenge to distinguish the inliers and outliers, and once the wrong inter-robot loop closures are used to optimize the pose graph, which can seriously corrupt the fused global map. Existing works mainly rely on the consistency of spatial dimension to select inter-robot measurements, while it does not always hold. In this paper, we propose a fast inter-robot loop closure selection method that integrates the consistency and topology relationship of inter-robot measurements, which both conform to the continuity characteristics of similar scenes and spatiotemporal consistency. Firstly, a clustering method integrating topology correctness of inter-robot loop closures is proposed to split the entire measurement set into multiple clusters. Then, our method decomposes the traditional high-dimensional consistency matrix into the sub-matrix blocks corresponding to the overlapping trajectory regions. Finally, we define the weight function to find the topologically correct and consistent subset with the maximum cardinality, then convert the weight function to the maximum clique problem from graph theory and solve it. We evaluate the performance of our method in a simulation and in a real-world experiment. Compared to state-of-the-art methods, the results show that our method can achieve competitive performance in accuracy while reducing computation time by 75%.

Vision-based trajectory monitoring for assembly alignment of precast concrete bridge components

The prefabricated structures are popularly used because of their high efficiency and it is important to ensure their installation quality. The current installation is guided by visual observation and total station, which is inaccurate and time-consuming. This study presents a stereo-vision-based pose measurement technique for guiding the installation to address the problems. The contributions are as follows: 1) A framework of a vision-based 6-DOF pose monitoring system is designed to track the precast member continuously; 2) A two-level adjustment strategy is suggested by transferring the relative pose measurement between the precast member and existing structure to the process to determine the relative relations of two coordinate systems; 3) A robust deep-learning-based ellipse detection method is proposed to identify and classify the artificial targets accurately. The proposed technique is evaluated in both indoor experiments and the construction of an expressway viaduct to demonstrate its accuracy and effectiveness.

Thermal stability measurement of terminal face relative pose for complex structure based on coordinate measuring machine

Abstract The coordinate measuring machine has high measurement accuracy, but it is only suitable for the precise measurement of the geometric size of the component in the normal temperature environment, and some complex structures need to assess the stability of the terminal face relative pose in the high and low temperature environment. In order to apply the high-precision measurement of the coordinate measuring machine to the thermal stability evaluation of the relative pose of the terminal face of complex structures, an extended temperature control method based on the coordinate measuring machine is proposed. The method realizes the loading control of the temperature environment of the specimen by the mobile temperature control module and the temperature control medium circulation module. The flexible connection mode is used between the temperature control module and the temperature control medium circulation module to reduce the influence of mechanical vibration on the measurement accuracy. The mixing fan and the temperature control medium dryer are installed in the pipeline to avoid frosting and dewing in the temperature control box and ensure the uniform distribution of the temperature field, so as to realize the accurate measurement of the relative pose of the terminal face of the complex structure under the high and low temperature environment by the coordinate measuring machine. The method is applied to the thermal stability test of the relative pose of the end face of a joint component, and the test results are good, which provides a new idea for the extended application of coordinate measuring machines.

Cooperative Localization of 3D Vehicle Networks - The Dynamic Model

Cooperative localization (CL) algorithms have mainly focused on two-dimensional (2D) vehicle models and have almost uniformly employed only the kinematic model with velocities as inputs, making these algorithms unsuitable for force control problems. We have previously extended a CL algorithm to include a three-dimensional (3D) vehicle kinematic model with the use of quaternions to avoid singularities. In this paper, we extend the algorithm to a full 3D nonlinear dynamic vehicle model, again using quaternions. We evaluate the effectiveness of a CL algorithm and assess its benefits, when the full dynamic model is employed, by running a series of Monte Carlo type simulations with multiple relative pose measurements.

Autonomous Target Docking of Nonholonomic Mobile Robots Using Relative Pose Measurements

This article investigates the target docking problem for nonholonomic mobile robots using relative position and orientation measurements of the target. Considering the fact that target docking operations generally happen in unstructured environments where obstacles are left lying around, the whole docking process is divided into two phases, namely the approaching phase and the autonomous docking phase. In the approaching phase, the robot navigates in the operational environment until it reaches a switching region around the target, which triggers the autonomous docking phase. To account for the noise-corrupted and intermittent target observations, an extended Kalman filter-based relative pose estimation algorithm is designed to estimate the position and orientation of the target in the local reference frame of the robot. To achieve a perfect docking operation in the autonomous docking phase, which requires a zero-impact angle and a zero-lateral offset at the final engagement stage, a practical solution is proposed where motion planning and tracking control of the robot are incorporated into a unified scheme. Unlike most of the existing docking approaches in the literature which only account for static targets, our proposed strategy addresses the moving target docking problem as well. Real robot experiments have been performed to demonstrate the effectiveness of the proposed method.

基于点线特征结合的单目相对位姿测量方法

基于点线特征结合的单目相对位姿测量方法

Relative pose measurement of binocular vision based on feature circle

In traditional pose measurement algorithms, there are many visual measurement solutions for feature points and lines but few for feature circles. In this paper, the feature circle is described by the rotation characteristic of a point around a rotating axis in dual quaternion algebra. Dual vectors are used to describe the feature points, and Plücker lines are used to describe the rotation axis; that is, the dynamic characteristic of the feature point rotating around the Plücker line is used to describe the feature circle, and then the relative position and attitude information between the leader and follower rigid bodies are calculated by the principle of binocular vision measurement. The simulation results show that the algorithm can meet the requirements of the relative pose measurement accuracy of rigid bodies.

A Pose Measurement Method of a Space Noncooperative Target Based on Maximum Outer Contour Recognition

The relative pose (position and attitude) measurement of space noncooperative targets is very important for on-orbit servicing activities, such as target tracking, approaching, and capturing. The traditional methods rarely consider the instability of feature extraction and image blurring caused by target tumbling. In this paper, a method based on the maximum outer contour (MOC) recognition is proposed to measure the pose of the target. Different feature extraction algorithms can simultaneously achieve close- and long-range measurement tasks. First, the trailing image is restored by the image enhancement method. Second, the “rough + fine” combination recognition method is used for contour extraction and connected component labeling of the restored image, and the target feature extraction time is reduced to one-third of traditional methods. Furthermore, the elliptical surface on the MOC is fitted by the least squares method (LSM), and the ellipse parameters (i.e., the center position, the long- and short-axes, and the deflection angle) are extracted. The accuracy of the target recognition is improved. Third, for the close-range measurement, based on the detected ellipse parameter, the pose of the noncooperative target is solved by the binocular imaging algorithm of the space circle; for the long-range measurement, the contour centroid of the target is calculated by the detected MOC, and the position of the target is solved by the LSM. Moreover, the effectiveness of the method is verified by the OpenSceneGraph numerical simulation system. Finally, an experimental system consisting of a binocular camera, a UR5 manipulator, and a satellite mockup was built. The experimental results verified the proposed method.

Rock‐ring detection accuracy improvement in infrared satellite image with sub‐pixel edge detection

The projection of space circle can be used for relative pose measurement of satellite targets. The accuracy of elliptical parameters plays an important role in the accuracy of pose recovery. However, the quality of space visible and infrared image is poor. The traditional ellipse detection method is mainly based on the edge of pixel-accuracy-wise and the ellipse accuracy is low, resulting in pose measurement errors. In this study, a sub-pixel-accuracy-wise edge-based ellipse fitting method is proposed to improve the ellipse accuracy. To realise this goal, we improved the arc-based ellipse detection method and designed sub-pixel-edge-based ellipse detection method. Experimental results show that the ellipse accuracy fitted by sub-pixel edge coordinates is at least 50% higher than that by pixel edge coordinates, especially when the ellipse is incomplete. The author's method is the first one to propose and verify the sub-pixel edge coordinate which contributes to enhancing ellipse accuracy.

A Novel Method for Measuring Drogue-UAV Relative Pose in Autonomous Aerial Refueling Based on Monocular Vision

The key to the docking phase of autonomous aerial refueling missions is the relative pose measurement between the drogue and unmanned aerial vehicles. A novel measurement method for drogue-UAV (unmanned aerial vehicle) relative poses based on monocular vision is proposed in this paper. An adaptive arc-level structural feature extraction algorithm is applied to obtain the features that can be used for the measurements. In this algorithm, the projection of the end plane of the drogue in the image plane is accurately extracted based on an extraction quality metric. The projection contour and centroid of the internal black part of the drogue form the structural feature for the measurements. A robust pose estimation algorithm is presented to estimate the relative pose between the drogue and the UAV. The pose can be solved using a cone that is composed of the optical center of the camera and the structural feature. A Kalman filter is then applied to robustly estimate the pose of the drogue. A simulation and ground experiment are used to verify the robustness and effectiveness of the proposed method, which achieves good performance compared with other methods.

Relative pose measurement of moving rigid bodies based on binocular vision

To solve the shortcomings of the traditional monocular vision measurement algorithm, which needs to be solved iteratively, this paper proposes a method based on binocular vision for measuring the relative pose of moving rigid bodies. In this paper, the coordinates of the feature points of the follower rigid body coordinate system are obtained by using the method of least squares. At the same time, combined with the dual quaternion algorithm, the leader and follower rigid bodies’ coordinate systems are uniformly described, and the model takes the minimum sum of squared errors as the objective function and solves the pose parameters by the dual quaternion method. The simulation results show that the dual quaternion algorithm not only overcomes the shortcomings of the traditional quaternion and translation vector split description coordinate system transformation but also has strong stability; the relative position error is less than 0.05 m, and the relative attitude error is less than 0.2°, which can meet the measurement accuracy requirements of the relative pose of the moving rigid body.

Metrological Characterization of a Vision-Based System for Relative Pose Measurements with Fiducial Marker Mapping for Spacecrafts

An improved approach for the measurement of the relative pose between a target and a chaser spacecraft is presented. The selected method is based on a single camera, which can be mounted on the chaser, and a plurality of fiducial markers, which can be mounted on the external surface of the target. The measurement procedure comprises of a closed-form solution of the Perspective from n Points (PnP) problem, a RANdom SAmple Consensus (RANSAC) procedure, a non-linear local optimization and a global Bundle Adjustment refinement of the marker map and relative poses. A metrological characterization of the measurement system is performed using an experimental set-up that can impose rotations combined with a linear translation and can measure them. The rotation and position measurement errors are calculated with reference instrumentations and their uncertainties are evaluated by the Monte Carlo method. The experimental laboratory tests highlight the significant improvements provided by the Bundle Adjustment refinement. Moreover, a set of possible influencing physical parameters are defined and their correlations with the rotation and position errors and uncertainties are analyzed. Using both numerical quantitative correlation coefficients and qualitative graphical representations, the most significant parameters for the final measurement errors and uncertainties are determined. The obtained results give clear indications and advice for the design of future measurement systems and for the selection of the marker positioning on a satellite surface.

Particle filter-based relative rolling estimation algorithm for non-cooperative infrared spacecraft

The issue of feature point mismatching among infrared image sequence would bring big challenge to estimating the relative motion of non-cooperative spacecraft for it couldn’t provide the prior knowledge about its geometric structure and motion pattern. The paper introduces particle filter to precisely match the feature points within a desired region predicted by a kinetic equation, and presents a least square estimation-based algorithm to measure the relative rolling motion of non-cooperative spacecraft. The state transition equation and the measurement update equation of non-cooperative spacecraft are represented by establishing its kinetic equations, and then the relative pose measurement is converted to the maximum posterioriprobability estimation via assuming the uncertainties about geometric structure and motion pattern as random and time-varying variables. These uncertainties would be interpreted and even solved through continuously measuring the image feature points of the rotating non-cooperative infrared spacecraft. Subsequently, the feature point is matched within a predicted region among sequence infrared image using particle filter algorithm to overcome the position estimation noise caused by the uncertainties of geometric structure and motion pattern. Finally, the position parameters including rotation motion are estimated by means of solving the minimum error of feature point mismatching using least square estimate theory. Both simulated and real infrared image sequences are induced in the experiment to evaluate the performance of the relative rolling estimation, and the experimental data show that the rolling motion estimated by the proposed algorithm is more robust to the feature extraction noise and various rotation speed. Meanwhile, the relative rolling estimation error would increase dramatically with distance and rotation speed increasing.

基于激光雷达的航天器相对位姿测量技术

The technology of the measurement of relative position and pose between two spacecrafts based on laser lidar is one of the key technologies in many space applications, such as on-orbit servicing to satellites of losing control, debris removal, etc. Firstly, the current research at home and abroad was described. Secondly, the measurement technology principle of relative position and pose was introduced and the point cloud registration method was highlighted. Finally, the simulation experiment of the spacecraft simulator was given. The result shows that measurement of relative position and pose between two spacecrafts based on laser lidar method is reasonable and feasible and it has high accuracy. It meets the relative navigation mission requirements and provides a greater reference for the implementation of engineering applications.