Pose estimation from corresponding point data

Solutions for four different pose estimation problems are presented. Closed-form least-squares solutions are given to the overconstrained 2D-2D and 3D-3D pose estimation problems. A globally convergent iterative technique is given for the 2D-perspective-projection-3D pose estimation problem. A simplified linear solution and a robust solution to the 2D-perspective-projection-2D-perspective-projection pose-estimation problem are also given. Simulation experiments consisting of millions of trials with varying numbers of pairs of corresponding points and varying signal-to-noise ratios (SNRs) with either Gaussian or uniform noise provide data suggesting that accurate inference of rotation and translation with noisy data may require corresponding point data sets with hundreds of corresponding point pairs when the SNR is less than 40 dB. The experimental results also show that the robust technique can suppress the blunder data which come from outliers or mismatched points