Fusion of absolute and incremental position and orientation sensors

We describe a theoretical basis for combining absolute and incremental position and orientation data, based on optimization and the maximum likelihood principle. We present algorithms for carrying out the computations, and discuss associated computational issues. We treat separately the translation and rotation problems. For the translation problem, we postulate that we have a sensor of absolute (position) and a sensor of first-difference (velocity) data. We also bring in the second-difference (acceleration) when we consider a possible dynamics assumption. For the rotation problem, we postulate only that we have a sensor of orientation and a sensor of first-order rotation changes. We obtain sensor averages by solving a nonlinearly constrained quadratic optimization problem.