Shape matching from grasp using a minimal representation size criterion

A robust polynomial-time algorithm for model-based pose estimation from tactile grasp data is presented. A minimal representation criterion is used to formulate the matching problem as a global optimization. The hypothesize-and-test paradigm is invoked to search for the optimal solution. A three-on-three match of model features to data features is used to reduce the transform search space to polynomial size. A polynomial-time assignment algorithm is used to compute the optimal correspondence for each hypothesized pose. The strength of the algorithm lies in its ability to perform with noisy or incomplete data and with missing or spurious features. It is capable of rejecting outliers and finding partial matches, and it produces sensible results even in the presence of large noise. The algorithm has been implemented and tested on actual data