In-Hand Grasping Pose Estimation Using Particle Filters in Combination with Haptic Rendering Models

Abstract

Specialized grippers used in the industry are often restricted to specific tasks and objects. However, with the development of dexterous grippers, such as humanoid hands, in-hand pose estimation becomes crucial for successful manipulations, since objects will change their pose during and after the grasping process. In this paper, we present a gripping system and describe a new pose estimation algorithm based on tactile sensory information in combination with haptic rendering models (HRMs). We use a 3-finger manipulator equipped with tactile force sensing elements. A particle filter processes the tactile measurements from these sensor elements to estimate the grasp pose of an object. The algorithm evaluates hypotheses of grasp poses by comparing tactile measurements and expected tactile information from CAD-based haptic renderings, where distance values between the sensor and 3D-model are converted to forces. Our approach compares the force distribution instead of absolute forces or distance values of each taxel. The haptic rendering models of the objects allow us to estimate the pose of soft deformable objects. In comparison to mesh-based approaches, our algorithm reduces the calculation complexity and recognizes ambiguous and geometrically impossible solutions.