A novel one shot object profilometry system using Direct Sequence Spread Spectrum profilometry

Abstract

In this paper a new method of determining 3D object shape using patterns derived from Direct Sequence Spread Spectrum (DSSS) and an Unscented Kalman Filter (UKF) is presented. First a binary message is encoded via Binary Phase Shift Keying (BPSK), and spread using pseudo-random spreading to create a pattern. An Iterative Unscented Kalman Filter (IUKF) is then used to determine the deformation in the pattern due to an object, and a Kalman smoother is used to reduce noise in the deformation estimation. Results show that the iterative UKF is able to determine the deformation in the pattern with a lower absolute error residual between the ground truth and estimated deformation than the non-iterated UKF. Results of the accompanying Cramer-Rao lower bounds show that the lower bound on the DSSS Pattern is lower than that of the fringe pattern.