<title>Automatic inspection of industrial parts using 3D optical range sensor</title>

Abstract

A new automated inspection algorithm of industrials parts using a 3D laser range sensor is described. The input to the program is a tessellated representation of the part at a desired resolution saved in a neutral STL format and an unordered series of measurements produced by a 3D optical sensor. The output is a colored version of the model indicating the level of discrepancy between the measured points and the model. Using this coloring scheme, an operator or a robotic system can rapidly identify defective parts or monitor process drift on a production line. At the base of the method, a new robust correspondence algorithm which can find the rigid transformation between the tessellated model of the part and the measured points, is presented. This method is based on a least median square norm capable of a robustness of up to 50%. The robustness of the method is essential since one cannot guarantee that in practice, all the points in the measured set belong to the model. These types of algorithms are usually quite costly in computational complexity, but we will show that one can speed-up these algorithms by using the well-known iterative closest points algorithm and a multiresolution scheme based on voxels.