Contact-based in-hand pose estimation using Bayesian state estimation and particle filtering

In industrial assembly tasks, the position of an object grasped by the robot has to be known with high precision in order to insert or place it. In real applications, this problem is commonly solved by jigs that are specially produced for each part. However, they significantly limit flexibility and are prohibitive when the target parts change often, so a flexible method to localize parts with high accuracy after grasping is desired. To solve this problem, we propose a method that can estimate the position of an object in the robot’s hand to sub-millimeter precision, and can improve its estimate incrementally, using only minimal calibration and a force sensor. Our method is applicable to any robotic gripper and any rigid object that the gripper can hold, and requires only a force sensor. We demonstrate that the method can determine the position of an object to a precision of under 1 mm without using any part-specific jigs or equipment.