An Industrial Robot as Part of an Automatic System for Geometric Reverse Engineering

In many areas of engineering, medical sciences or art there is a strong demand to create appropriate computer representations of existing objects from large sets of measured 3D data points. This process is called Geometric Reverse Engineering (GRE). GRE is an important topic in computer vision but due to the advances in high accuracy laser scanning technology, GRE is now also an important topic in the geometric modelling community where it is used to generate accurate and topologically consistent CAD models for manufacturing and prototyping. Commercial GRE systems are available but still developing. There are many research groups dealing with the GRE problem and its applications in universities all over the world, e.g., Geometric Modelling Laboratory at the Hungarian Academy of Sciences and Geometric Computing & Computer Vision at Cardiff university. The CAD technology research group at Örebro University in Sweden started its GRE research in 2001. Its work is focused on problems related to automatic GRE of unknown objects. A laboratory environment has been created with a laser profile scanner mounted on an industrial robot controlled and integrated by software based on the open source CAD system Varkon. The basic principles and the system were described by (Larsson & Kjellander, 2004). They give the details of motion control and data capturing in (Larsson & Kjellander, 2006) and present an algorithm for automatic path planning in (Larsson & Kjellander, 2007). Automatic GRE requires an automatic measuring system and the CAD research group has chosen to use the industrial robot as the carrier of the laser scanner. This has many advantages but a problem with the approach is that the accuracy of the robot is relatively low as compared to the accuracy of the laser scanner. It is therefore of certain interest to investigate how different parts of the system will influence the final accuracy of GRE operations. The author knowing that the scanner is more accurate than the robot (Rahayem et al., 2007) it is also of interest to investigate if and how 2D profile data from the scanner can be used to increase the accuracy of GRE operations. Section 3 presents the measuring system. A detailed investigation of the accuracy of the system is given in (Rahayem et al., 2007). Section 4 includes an introduction of a typical GRE operation (segmentation). The accuracy of planar segmentation based on 3D point clouds is investigated and published in (Rahayem