In this thesis we implemented an automated scanning system for 3D object reconstruction. This system is composed of a KUKA LWR 4+ arm with Microsoft Kinect cameras placed on its extreme and thus, in an eye-in-hand con guration. We implemented the system in ROS using Kinect Fusion software with extra features added by R. Monica's previous work [16] and MoveIt! ROS libraries [29] to control the robot movement with motion planning. To connect these nodes, we have coded a suite using ROS and MATLAB to easily operate them as well as including new features, such as an original view planner that outperforms the commonly used Next-Best-View planner. This suite incorporates a Graphical User Interface that allows new users to easily perform the reconstruction tasks. The new view planner developed in this work, called Best-Path planner, o ers a new approach using a modi ed Dijkstra algorithm. Among its bene ts, Best-Path planner o ers an optimized way to scan the objects preventing the camera to cross again the areas which have already been scanned. Moreover, viewpoint location and orientation have been studied in depth in order to obtain the most natural movements and get the best results. For this reason, this new planner makes the scanning procedure more robust as it assures trajectories through these optimized viewpoints, so the camera is always looking towards the object maintaining the optimal sensing distances. As this project is focused on its later utility in the Intelligent Robotics Laboratory, we uploaded all the source code in the Aalto GitLab repositories [37] with installation instructions and user guides to show the di erent features that the suite o ers.