Statistical Partial Constraints for 3D Model Matching and Pose Estimation Problems

We explore the potential of variance matrices to represent not just statistical error on object pose estimates but also partially constrained degrees of freedom. Using an iterated extended Kalman filter as an estimation tool, we generate, combine and predict partially constrained pose estimates from 3D range data. We find that partial constraints on the translation component of pose which occur frequently in practice are handled well by the method. However, coupled partial constraints between rotation and translation are, in general, non-linear and cannot be represented by this method.