文档字母次数统计

The industrial manufacturing process has changed over the years. Driven by the need for higher productivity, development cycles became much faster and time-to-market is more important than ever before. Feature based and parametric CAD systems allow rapid changes, simulation methods help to guarantee technical soundness and new technologies such as rapid prototyping are used to establish new production processes. In the context of rapid product development, quality control becomes a crucial and time-critical factor in development as well as in the production process. Traditionally, coordinate measurement machines (CMMs) are used for mechanical part inspection. CMMs are well established and widely accepted in industry, but suffer from limitations such as high cost and low measurement speed, corresponding to a long validation time and therefore do not meet the requirements formulated above. On the other hand, optical 3-D sensors measure the shape of objects, without the need to physically probe surfaces. Modern optical sensors are faster, cheaper and provide a higher measurement density than conventional techniques and are therefore ideally suited for applications like reverse engineering, rapid validation (including soft or deformable surfaces), digitization of VR models and guidance for industrial robots. After some years of skepticism, optical measurement systems are starting to replace the touch-trigger probes which have been widely used in industry to date. The performance of such a system depends both on the type and number of sensors and on the configuration of the entire system. The processing steps needed to convert collected image data to three-dimensional coordinates play another important role. However, system calibration is without doubt the limiting factor for the accuracy of most 3-D measurement systems. In Brenner et al. (1999), we reported on the photogrammetric calibration of an active optical triangulation sensor and compared the results to a direct calibration method, namely polynomial depth calibration. Since then, we have enhanced our hardware setup, automated most steps of the calibration procedure and developed a new method to solve the correspondence problem with sub-pixel accuracy. The remainder of this paper is organized as follows. We first describe our hardware setup in Section 2. Section 3 details the patterns and the processing steps we use to solve the correspondence problem. The issue of calibration is addressed in Section 4. In Section 5 experimental results are given and compared to our previous measurement system. Section 6 summarizes the results we have obtained.