Three-dimensional rigid motion estimation using genetic algorithms from an image sequence in an active stereo vision system

Abstract

This paper proposes a method for estimating the three-dimensional (3D) rigid motion parameters from an image sequence of a moving object. The 3D surface measurement is achieved using an active stereovision system composed of a camera and a light projector, which illuminates the objects to be analyzed by a pyramid-shaped laser beam. By associating the laser rays with the spots in the two-dimensional image, the 3D points corresponding to these spots are reconstructed. Each image of the sequence provides a set of 3D points, which is modeled by a B-spline surface. Therefore, estimating the 3D motion between two images of the sequence boils down to matching two B-spline surfaces. We consider the matching environment as an optimization problem and find an optimal solution using genetic algorithms. A chromosome is encoded by concatenating seven binary coded parameters, the angle, and the three components of the rotation vector axis, and the three translation vector components. We have defined an original fitness function for calculating the similarity measure between two surfaces. Experimental results with real and synthetic image sequences are presented to show the effectiveness and the robustness of the method.