Active Recognition and Manipulation for Mobile Robot Bin Picking

Grasping individual objects from an unordered pile in a box has been investigated in stationary scenarios so far. In this work, we present a complete system including active object perception and grasp planning for bin picking with a mobile robot. At the core of our approach is an efficient representation of objects as compounds of simple shape and contour primitives. This representation is used for both robust object perception and efficient grasp planning. For being able to manipulate previously unknown objects, we learn object models from single scans in an offline phase. During operation, objects are detected in the scene using a particularly robust probabilistic graph matching. To cope with severe occlusions we employ active perception considering not only previously unseen volume but also outcomes of primitive and object detection. The combination of shape and contour primitives makes our object perception approach particularly robust even in the presence of noise, occlusions, and missing information. For grasp planning, we efficiently pre-compute possible grasps directly on the learned object models. During operation, grasps and arm motions are planned in an efficient local multiresolution height map. All components are integrated and evaluated in a bin picking and part delivery task.