Global alignment of sensor positions with noisy motion measurements

We investigate the global alignment of some 3-D spatial points, knowing the motion between pairwise nearby positions. A common application is to determine the trajectory of a mobile vision-based system from the scene images acquired along its track. Exploiting redundant measurements and the rigid body transformation P_j = R^(i,J)P_i+t^(i,J) as the observation model, we apply the mixed-model least squares estimation paradigm to develop recursive estimation algorithms under various scenarios. Results of experiments are given to demonstrate the performance for different noise levels in the observations and the improvements in the position estimation.