Monocular vision measurement system for the position and orientation of remote object

Tao Zhou; Changku Sun; Shan Chen

doi:10.1117/12.791428

5 March 2008 Monocular vision measurement system for the position and orientation of remote object

Tao Zhou, Changku Sun, Shan Chen

Proceedings Volume 6623, International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing; 662311 (2008) https://doi.org/10.1117/12.791428
Event: International Symposium on Photoelectronic Detection and Imaging: Technology and Applications 2007, 2007, Beijing, China

Abstract

The high-precision measurement method for the position and orientation of remote object, is one of the hot issues in vision inspection, because it is very important in the field of aviation, precision measurement and so on. The position and orientation of the object at a distance of 5 m, can be measured by near infrared monocular vision based on vision measurement principle, using image feature extraction and data optimization. With the existent monocular vision methods and their features analyzed, a new monocular vision method is presented to get the position and orientation of target. In order to reduce the environmental light interference and make greater contrast between the target and background, near infrared light is used as light source. For realizing automatic camera calibration, a new feature-circle-based calibration drone is designed. A set of algorithms for image processing, proved to be efficient, are presented as well. The experiment results show that, the repeatability precision of angles is less than 8"; the repeatability precision of displacement is less than 0.02 mm. This monocular vision measurement method has been already used in wheel alignment system. It will have broader application field.

Citation Download Citation

Tao Zhou, Changku Sun, and Shan Chen "Monocular vision measurement system for the position and orientation of remote object", Proc. SPIE 6623, International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing, 662311 (5 March 2008); https://doi.org/10.1117/12.791428

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