UCL  IRIS
Institutional Research Information Service
UCL Logo
Please report any queries concerning the funding data grouped in the sections named "Externally Awarded" or "Internally Disbursed" (shown on the profile page) to your Research Finance Administrator. Your can find your Research Finance Administrator at https://www.ucl.ac.uk/finance/research/rs-contacts.php by entering your department
Please report any queries concerning the student data shown on the profile page to:

Email: portico-services@ucl.ac.uk

Help Desk: http://www.ucl.ac.uk/ras/portico/helpdesk
Publication Detail
A webcam photogrammetric method for robot calibration
  • Publication Type:
    Conference
  • Authors:
    Sargeant B, Hosseininaveh A. A, Erfani T, Robson S, Boehm J
  • Publication date:
    26/07/2013
  • Published proceedings:
    Proceedings of SPIE - The International Society for Optical Engineering
  • Volume:
    8791
  • ISBN-13:
    9780819496072
  • Status:
    Published
  • Print ISSN:
    0277-786X
Abstract
This paper describes a strategy for accurate robot calibration using close range photogrammetry. A 5-DoF robot has been designed for placement of two web cameras relative to an object. To ensure correct camera positioning, the robot is calibrated using the following strategy. First, a Denavit-Hartenberg method is used to generate a general kinematic robot model. A set of reference frames are defined relative to each joint and each of the cameras, transformation matrices are then produced to represent change in position and orientation between frames in terms of joint positions and unknown parameters. The complete model is extracted by multiplying these matrices. Second, photogrammetry is used to estimate the postures of both cameras. A set of images are captured of a calibration fixture from different robot poses. The camera postures are then estimated using bundle adjustment. Third, the kinematic parameters are estimated using weighted least squares. For each pose a set of equations are extracted from the model and the unknown parameters are estimated in an iterative procedure. Finally these values are substituted back into the original model. This final model is tested using forward kinematics by comparing the model's predicted camera postures for given joint positions to the values obtained through photogrammetry. Inverse kinematics is performed using both least squares and particle swarm optimisation and these techniques are contrasted. Results demonstrate that this photogrammetry approach produces a reliable and accurate model of the robot that can be used with both least squares and particle swarm optimisation for robot control. © 2013 SPIE.
Publication data is maintained in RPS. Visit https://rps.ucl.ac.uk
 More search options
There are no UCL People associated with this publication
University College London - Gower Street - London - WC1E 6BT Tel:+44 (0)20 7679 2000

© UCL 1999–2011

Search by