Generation of Independent Contact Regions on objects reconstructed from noisy real-world range data

The synthesis and evaluation of multi-fingered grasps on complex objects is a challenging problem that has received much attention in the robotics community. Although several promising approaches have been developed, applications to real-world systems are limited to simple objects or gripper configurations. The paradigm of Independent Contact Regions (ICRs) has been proposed as a way to increase the tolerance to grasp positioning errors. This concept is well established, though only on precise geometric object models. This work is concerned with the application of the ICR paradigm to models reconstructed from real-world range data. We propose a method for increasing the robustness of grasp synthesis on uncertain geometric models. The sensitivity of the ICR algorithm to noisy data is evaluated and a filtering approach is proposed to improve the quality of the final result.