RobotFusion: Grasping with a Robotic Manipulator via Multi-view Reconstruction

Daniele De Gregorio, Federico Tombari, Luigi di Stefano

Abstract: We propose a complete system for 3D object reconstruction and grasping based on an articulated robotic manipulator. We deploy an RGB-D sensor as an end effector placed directly on the robotic arm, and process the acquired data to perform multi-view 3D reconstruction and object grasping. We leverage the high repeatability of the robotic arm to estimate 3D camera poses with millimeter accuracy and control each of the six sensor’s DOF in a dexterous workspace. Thereby, we can estimate camera poses directly by robot kinematics and deploy a Truncated Signed Distance Function (TSDF) to accurately fuse multiple views into a unified 3D reconstruction of the scene. Then, we propose an efficient approach to segment the sought objects out of a planar workbench as well as a novel algorithm to automatically estimate grasping points.