Robust estimation of rigid-body 3-D motion parameters based on point correspondences

The estimation of rigid-body 3-D motion parameters using point corresponden ces from a pair of images under perspective projection is, typically, very sensitive to noise. In this paper, we present a novel robust method combini ng two approaches: 1) the SVD analysis of a linear operator resulting from the feature points and the displacement vectors and 2) a modified version o f the well-known weighted least-squares method proposed by Huber in the con text of robust statistics. We give a detailed rank analysis of the involved linear operator and study the effects of noise. We also propose a robust m ethod guided by the structure of this operator, using weighted least square s and data partitioning. The method has been tested on artificial data and on real image sequences showing a remarkable robustness, even in the presen ce of up to 50% outliers in the data set.