Noncollinear Feature Points Research Articles

Motion and inertial parameter estimation of non-cooperative target on orbit using stereo vision

In the on-orbit servicing missions, autonomous close proximity operations require knowledge of the target’s motion and inertial parameters in order to safely interact with the target. In particular, the motion and parameter estimation of an uncooperative target is a challenging task because of the lack of some prior information about the target in unfamiliar environments. In this paper, a novel method is developed for an accurate estimation of the motion and inertial parameters of an unknown target using stereo vision measurements only. Instead of using relative position and pose as observation information, this paper chooses three non-collinear feature points on the target as estimation measurements. This treatment allows us to estimate the principal axis of inertia of the target directly, and avoid estimating the quaternion between the target measurement coordinate system and the principal axis of inertia coordinate system, which is a bilinear problem, and is difficult to obtain global convergence. Based on the relative kinematics and dynamics equations of the target, the state equation and observation equation are derived, and the Extended Kalman Filter (EKF) is designed. As a result, the developed algorithm realizes the estimation of the complete unknown information of the target, including relative position, relative velocity, attitude quaternion, angular velocity, the direction of the principal axis of inertia, inertia ratio, and the position of center of mass. Numerical simulations and experimental results verify the convergence and effectiveness of the proposed filter estimation method.

High-precision six-degree-of-freedom pose measurement and grasping system for large-size object based on binocular vision

PurposeThe positioning and grasping of large-size objects have always had problems of low positioning accuracy, slow grasping speed and high application cost compared with ordinary small parts tasks. This paper aims to propose and implement a binocular vision-guided grasping system for large-size object with industrial robot.Design/methodology/approachTo guide the industrial robot to grasp the object with high position and pose accuracy, this study measures the pose of the object by extracting and reconstructing three non-collinear feature points on it. To improve the precision and the robustness of the pose measuring, a coarse-to-fine positioning strategy is proposed. First, a coarse but stable feature is chosen to locate the object in the image and provide initial regions for the fine features. Second, three circular holes are chosen to be the fine features whose centers are extracted with a robust ellipse fitting strategy and thus determine the precise pose and position of the object.FindingsExperimental results show that the proposed system has achieved high robustness and high positioning accuracy of −1 mm and pose accuracy of −0.5 degree.Originality/valueIt is a high accuracy method that can be used for industrial robot vision-guided and grasp location.

A New Feature Points Reconstruction Method in Spacecraft Vision Navigation

The important applications of monocular vision navigation in aerospace are spacecraft ground calibration tests and spacecraft relative navigation. Regardless of the attitude calibration for ground turntable or the relative navigation between two spacecraft, it usually requires four noncollinear feature points to achieve attitude estimation. In this paper, a vision navigation system based on the least feature points is designed to deal with fault or unidentifiable feature points. An iterative algorithm based on the feature point reconstruction is proposed for the system. Simulation results show that the attitude calculation of the designed vision navigation system could converge quickly, which improves the robustness of the vision navigation of spacecraft.

Three-dimensional precision analysis with rigid and compliant motions for sheet metal assembly

This paper quantifies the detailed assembly motions by taking into account the mating gaps and gravities of fixtures and sheet metals. Finite element (FE) models are firstly generated for fixtures and parts. Their nodes are ordered according to an appointed assembly sequence where the last assembled part is behind the first one by the assembly platform or mating surface. Three noncollinear feature points are selected from each last assembled part near the mating surface. Their translational displacements (gaps) represent the part rigid motion near that surface omitting the local joint deformation. Based on the given feature point gaps, kinematic formulations are proposed to compute the rigid motions for any FE nodes behind the mating surface in assembly sequence. Compliant motions are then reached by the modified FE analysis where variable mesh method is applied to change the FE nodal coordinates using the gap-induced rigid motions and deformations. Code integrations of sequence and parallel assemblies are finally proposed and validated via simulations and experiments. Results suggest the following: (1) the proposed method is effective and accurate for engineer application; (2) the integration approach requires to be further studied for the precision analysis of more complex assemblies and implies a feasible way for adding local deformations of joints to the deterministic dimensional precision analysis.

Constructing feature points for calibrating a structured light vision sensor by viewing a plane from unknown orientations

To calibrate a structured light vision sensor, it is necessary to obtain at least four non-collinear feature points that fall on the light stripe plane. We propose a novel method to construct non-collinear feature points used for calibrating a structured light vision sensor with a planar calibration object. After the planar calibration object is moved freely in the range of measuring of the structured light vision sensor at least twice, all the local world coordinates of the feature points falling on the light stripe plane can be readily obtained in site. The global world coordinates of the non-collinear feature points in the local world coordinate frame can be computed through the three-dimensional (3D) camera coordinate frame. A planar calibration object is designed according to the proposed approach to provide accurate feature points. The experiments conducted on a real structured light vision sensor that consists of a camera and a single-light-stripe-plane laser projector reveal that the proposed approach has high accuracy and is practical in the vision inspection applications. The proposed approach greatly reduces the cost of the calibration equipment and simplifies the calibrating procedure. It advances structured light vision inspection one step from laboratory environments to real world use.

3-D motion estimation using a sequence of noisy stereo images: models, estimation, and uniqueness results

A kinematic model-based approach for the estimation of 3-D motion parameters from a sequence of noisy stereo images is discussed. The approach is based on representing the constant acceleration translational motion and constant precession rotational motion in the form of a bilinear state-space model using standard rectilinear states for translation and quaternions for rotation. Closed-form solutions of the state transition equations are obtained to propagate the quaternions. The measurements are noisy perturbations of 3-D feature points represented in an inertial coordinate system. It is assumed that the 3-D feature points are extracted from the stereo images and matched over the frames. Owing to the nonlinearity in the state model, nonlinear filters are designed for the estimation of motion parameters. Simulation results are included. The Cramer-Rao performance bounds for motion parameter estimates are computed. A constructive proof for the uniqueness of motion parameters is given. It is shown that with uniform sampling in time, three noncollinear feature points in five consecutive binocular image pairs contain all the spatial and temporal information. Both nondegenerate and degenerate motions are analyzed. A deterministic algorithm to recover motion parameters from a stereo image sequence is summarized from the constructive proof.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A Computation for Robots to Orient and Position Hand-Held Workpieces

A computation is presented which enables robots with six degrees of freedom to orient and position workpieces against fixtures. It is assumed that the robot can grasp a single workpiece and that a vision system can determine the three-dimensional locations of three feature points on that workpiece. A feature point is a unique point on a workpiece, such as the apex of a cone, which may be located visually by examining neighboring edges or surfaces. The spatial relation between workpiece and fixture which is to be the goal of arm motion is specified by measurements taken as the workpiece is held with the correct posture by a programmer. These measurements are the spatial locations of three noncollinear feature points on the workpiece and three noncollinear feature points on the fixture. The computation presented adjusts for relative motion between fixture and robot if the vision system keeps track of the three feature points on the fixture. The solution to the equations presented is a set of arm link parameters which satisfy the goal of relative workpiece placement. The general form of the equations permits application of the computation to robots with different kinematic configurations.