We present a system for verifying the integrity of storage containers using a laser triangulation scanner, with
applications in nuclear security. Any intrusion into the container shell and subsequent reconstruction of the
surface inevitably leaves slight changes to the three-dimensional surface structure which the proposed system
can detect. The setup consists of a laser line scanner, mounted on a rotation stage. We propose an auto-calibration procedure for this system which − from several scans of a planar calibration target acquired from
different viewpoints − automatically determines the position and orientation of the rotation axis with respect
to the scanner coordinate frame. We further present an algorithm for the automatic registration of two 3D scans
of a cylindrical surface, not requiring any user interaction such as the identification of corresponding point pairs.
We show that the algorithm accurately aligns two scans of the same object, acquired from different viewpoints.
The accuracy of the overall system is dominated by the measurement uncertainty of the 3D scanner; residual
errors resulting from the calibration and registration are subordinate. The system can reliably detect changes in
the surface shape resulting from tampering.
The projection of structured light is a technique frequently used in computer vision to determine surface structure of scene objects. In this paper, higher level features are extracted from the images and used for a direct estimation of second-order object surface models. The algorithm is based upon a predictor-corrector approach which utilizes an initial estimate for the surface parameters, followed by iterative parameter refinement. A predicted passive image is generated using the current surface parameter estimates and significant features are extracted and compared with those in the true passive image. The extimated surface parameters are corrected based upon feature disparities. The algorithm is well-suited for a particular vision task involving recognition of cylindrical drums. In computer simulations and laboratory experiments, the algorithm was found to converge quickly and to yield accurate results.