Document Type

Article

Publication Date

5-20-2011

Abstract

Satellites are subject to harsh lighting conditions which make visual inspection difficult. Automated systems which detect changes in the appearance of a satellite can generate false positives in the presence of intense shadows and specular reflections. This paper presents a new algorithm which can detect visual changes to a satellite in the presence of these lighting conditions. The position and orientation of the satellite with respect to the camera, or pose, is estimated using a new algorithm. Unlike many other pose estimation algorithms which attempt to reduce image reprojection error, this algorithm minimizes the sum of the weighted 3-dimensional error of the points in the image. Each inspection image is compared to many different views of the satellite, so that pose may be estimated regardless of which side of the satellite is facing the camera. The features in the image used to generate the pose estimate are chosen automatically using the scale-invariant feature transform. It is assumed that a good 3-dimensional model of the satellite was recorded prior to launch. Once the pose between the camera and the satellite have been estimated, the expected appearance of the satellite under the current lighting conditions is generated using a raytracing system and the 3-dimensional model. Finally, this estimate is compared with the image obtained from the camera. The ability of the algorithm to detect changes in the external appearance of satellites was evaluated using several test images exhibiting varying lighting and pose conditions. The test images included images containing shadows and bright specular reflections.

DOI

10.1117/12.884151

Comments

Copyright 2011 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

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