The noise amplification index for optimal pose selection in robot calibration

Nahvi, Ali; Hollerbach, John M.

doi:10.1109/robot.1996.503848

Cited by 100 publications

(71 citation statements)

References 11 publications

(8 reference statements)

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“…(12) M is called the information matrix, moment matrix, or covariance matrix in different literatures. The minimization of a matrix has many expressions corresponding to different physical meanings.…”

Section: A Parameter Estimation Optimizationmentioning

confidence: 99%

“…The minimum singular value uses the worst observability of the parameter error as the criterion. Nahvi and Hollerbach [12] also proposed as an observability index the square of smallest non-zero singular value divided by the largest singular value of X, called the noise amplification index and referred to here as O 4 . Nahvi and Hollerbach [12] compared different observability indexes, and found that O 1 was worse than the others.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Observability index selection for robot calibration

Sun

Hollerbach

2008

2008 IEEE International Conference on Robotics and Automation

119

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Abstract-This paper relates 5 observability indexes for robot calibration to the "alphabet optimalities" from the experimental design literature. These 5 observability indexes are shown to be the upper and lower bounds of one another. All observability indexes are proved to be equivalent when the design is optimal after a perfect column scaling. It is shown that when the goal is to minimize the variance of the parameters, D-optimality is the best criterion. When the goal is to minimize the uncertainty of the end-effector position, E-optimality is the best criterion. It is proved that G-optimality is equivalent to E-optimality for exact design.

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Section: A Parameter Estimation Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Observability index selection for robot calibration

Sun

Hollerbach

2008

2008 IEEE International Conference on Robotics and Automation

119

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“…However perfect measurements do not exist and is necessary to choose best calibration poses. There exist approaches [4,32], where a noise amplification index is used to identify poses in which errors in the parameters of the robot are especially critical; this is given when a calibration pose is near from a singularity or from workspace boundaries. However in a practical calibration process, if poses are extremely near of singularities, those poses are able to produce unpredictable movements (due to kinematic errors).…”

Section: A Selection Of Calibration Posesmentioning

confidence: 99%

A method for kinematic calibration of a parallel robot by using one camera in hand and a spherical object

Traslosheros

Sebastián

Castillo

et al. 2011

2011 15th International Conference on Advanced Robotics (ICAR)

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Abstract-The main purpose of robot calibration is the correction of the possible errors in the robot parameters. This paper presents a method for a kinematic calibration of a parallel robot that is equipped with one camera in hand. In order to preserve the mechanical configuration of the robot, the camera is utilized to acquire incremental positions of the end effector from a spherical object that is fixed in the word reference frame. The positions of the end effector are related to incremental positions of resolvers of the motors of the robot, and a kinematic model of the robot is used to find a new group of parameters which minimizes errors in the kinematic equations. Additionally, properties of the spherical object and intrinsic camera parameters are utilized to model the projection of the object in the image and improving spatial measurements. Finally, the robotic system is designed to carry out tracking tasks and the calibration of the robot is validated by means of integrating the errors of the visual controller.

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“…where λ min and λ max are the smallest and largest eigenvalues of K. The NAI was described by Nahvi and Hollerbach [8], and Simon [12] found that are there were four important problems that must be addressed when using the NAI as a criterion for point selection.…”

Section: Strategies For Registration Point Selectionmentioning

confidence: 99%

Spatial-Stiffness Analysis of Surface-Based Registration

Ellis

2004

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

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Abstract. We have developed a new approach for preoperative selection of points from a surface model for rigid shape-based registration. This approach is based on an extension of our earlier spatial-stiffness model of fiducial registration. We compared our approach with the maximization of the noise-amplification index (NAI), using target registration accuracy (TRE) as our comparison measure, on models derived from computed tomography scans of volunteers. In this study, our approach was substantially less expensive to compute than maximizing the NAI and produced similar TREs with smaller variances. Optimal incremental selection shows promise for improving the preoperative selection of registration points for image-guided surgical procedures.

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The noise amplification index for optimal pose selection in robot calibration

Abstract: Abstract

Cited by 100 publications

References 11 publications

Observability index selection for robot calibration

Observability index selection for robot calibration

A method for kinematic calibration of a parallel robot by using one camera in hand and a spherical object

Spatial-Stiffness Analysis of Surface-Based Registration

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