Kalman filtering for dynamic pose and relative motion estimation in orbit

McTavish, Donald J.; Schumacher, Ralf; Okouneva, Galina

doi:10.5589/q08-001

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…A pose of a rigid body includes a position and an orientation, which describe six degrees of freedom (DOF) in 3D space with respect to a reference frame. Pose estimation for rigid bodies plays an important role in robotics, computer vision, and various positioning applications, such as visual servoing for robotics [14,[21][22][23], biomechanical motion [24], guidance for aircraft assembly automation [3], spacecraft docking and rendezvous [25], and satellite capture [26,27], etc. The conventional 3D-to-3D pose estimation deals with one 3D rigid body with respect to an observing system, i.e., the pose transformation after a motion of one body.…”

Section: Pose Estimationmentioning

confidence: 99%

Pose estimation for robotic percussive riveting.

Ye¹

2021

Preprint

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Recently, a robotic percussive riveting system has been developed at Ryerson University for an automation of percussive riveting process of aero-structural fastening assembly. The system consists of a robot holding a percussive riveting gun equipped with a rivet feeder, a gantry holding a working panel of aero-structure, and a position visual sensor. Prior to riveting, the robot is required to first position and then insert a rivet precisely into a hole on the panel without engaging the panel to prevent potential damage. The underlying challenges to precise insertion are various sources of system uncertainties, mainly including alignment errors among coordinate systems of the robot, panel and sensor, and relatively poor absolute positioning accuracy of the robot due to mechanical deflection, assembly clearance, and machining tolerance. For this reason, the research of relative pose estimation between the robot and panel has been carried out pertaining to these challenges. Essentially, pose estimation is proposed for robotic percussive riveting, which estimates the relative pose between two rigid bodies based on noisy visual measurements of point features on rigid bodies. Three categories can be classified, namely, static, dynamic, and robust pose estimation. Firstly, static pose estimation is the parameter estimation of static relative pose transformations among a number of frames, which solves the issue of alignment errors. Direct solutions of static relative pose estimation are derived based on least-square methods. Secondly, to tackle the issue of poor absolute positioning accuracy of the robot, dynamic relative pose estimation is proposed addressing a state estimation of relative poses during motion. Iterative extended Kalman filter method is adapted for the state estimation. Thirdly, for robustness against outliers of point measurements, robust pose estimation is proposed based on an outlier diagnosis using the technique of relaxation of rigid body constraints. Indeed, outlier diagnosis is a pre-processing of point measurements, in which outliers are detected and removed prior to the relative pose estimation. Further, a decorrelation method is proposed for measurement calibration using multivariate statistical analysis to find an optimal sensor-to-target configuration. As a result, each coordinate measurement is close to uncorrelated and it allows for a simple calibration.

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Section: Pose Estimationmentioning

confidence: 99%

Pose estimation for robotic percussive riveting.

Ye¹

2021

Preprint

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“…wherer i ,λ i , andξ i are the spacecraft polar accelerations, which are the second order time derivatives of the absolute range, absolute azimuth, and elevation shown in (10)- (11) respectively. Their corresponding equations arë…”

Section: Scenario Two-relative Position Estimation With Wlpsmentioning

confidence: 99%

“…In addition, the system becomes unobservable in a few cases, such as zero inclination. The extended Kalman filter (EKF) has been widely implemented into the nonlinear system to estimate the relative and absolute positions in SFF [5,[10][11][12]. The system nonlinearity causes the EKF estimation to be sensitive to initial condition errors, and the linearization step in the EKF may result in estimation instability.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Differential Geometric Filter for Spacecraft Formation Orbit Estimation

Goh

Abdelkhalik

Zekavat

2012

International Journal of Aerospace Engineering

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The differential geometric filter is implemented to estimate the absolute and relative positions of the spacecraft in a formation. The extended Kalman Filter is also implemented as a benchmark for the differential geometric estimation. Only relative positions between the spacecraft are measured. Relative positions are measured using a wireless local positioning system (WLPS) installed in all spacecraft. Two different scenarios are studied: (1) the observations include WLPS measurements only and (2) the observations include WLPS measurements in addition to measurements for the absolute position of one spacecraft made by a radar that takes measurements from the earth’s surface. Results show that the differential geometric estimation has better stability performance and a faster convergence rate compared to the extended Kalman filter.

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“…The Extended Kalman Filter (EKF) has been widely implemented into the nonlinear system to estimate the relative and absolute positions in SFF [7,16,78,79]. The system nonlinearity causes the EKF estimation to be sensitive to initial condition errors, and the linearization step in the EKF may result in estimation instability.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for spacecraft formation flying navigation based on wireless positioning system measurements

Goh¹

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Kalman filtering for dynamic pose and relative motion estimation in orbit

Cited by 5 publications

References 4 publications

Pose estimation for robotic percussive riveting.

Pose estimation for robotic percussive riveting.

Implementation of a Differential Geometric Filter for Spacecraft Formation Orbit Estimation

Algorithms for spacecraft formation flying navigation based on wireless positioning system measurements

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