Combining Visual and Inertial Features for Efficient Grasping and Bin-Picking

Grasping objects is a well-known problem in robotics. If the objects to be grasped are known, usually they are to be placed at a desired position in a desired orientation. Therefore, the object pose w.r.t the gripper has to be known before placing the object. In this paper we propose a simple and efficient, yet robust approach to this challenge, which can (nearly) eliminate dead times of the employed manipulator hence speeding up the process significantly. Our approach is based on the observation that the problem of finding a pose at which the object can be grasped and the problem of computing the pose of the object w.r.t. the gripper can be solved separately at different stages. Special attention is paid to the popular bin-picking problem where this strategy shows its full potential. To reduce the overall cycle time, we estimate the grasp pose after the object has been grasped. Our estimation technique relies on the inertial parameters of the object-instead of visual features-which enables us to easily incorporate pose changes due to grasping. Experiments show that our approach is fast and accurate. Furthermore, it can be implemented easily and adapted to diverse pick and place tasks with arbitrary objects.