Differential Information Aided 3-D Registration for Accurate Navigation and Scene Reconstruction

Abstract

A novel 3-dimensional (3-D) alignment method for point-cloud registration is proposed where the time-differential information of the measured points is employed. The new problem turns out to be a novel multi-dimensional optimization. Analytical solution to this optimization is then obtained, which sets the ground of further correspondence matching using k-D trees. Finally, via many examples, we show that the new method owns better registration accuracy in real-world experiments.