Abstract
Photogrammetry methods are being used more and more as a 3D technique for large scale metrology applications in industry. Optical targets are placed on an object and images are taken around it, where measuring traceability is provided by precise off-process pre-calibrated digital cameras and scale bars. According to the 2D target image coordinates, target 3D coordinates and camera views are jointly computed. One of the applications of photogrammetry is the measurement of raw part surfaces prior to its machining. For this application, post-process bundle adjustment has usually been adopted for computing the 3D scene. With that approach, a high computation time is observed, leading in practice to time consuming and user dependent iterative review and re-processing procedures until an adequate set of images is taken, limiting its potential for fast, easy-to-use, and precise measurements. In this paper, a new efficient procedure is presented for solving the bundle adjustment problem in portable photogrammetry. In-process bundle computing capability is demonstrated on a consumer grade desktop PC, enabling quasi real time 2D image and 3D scene computing. Additionally, a method for the self-calibration of camera and lens distortion has been integrated into the in-process approach due to its potential for highest precision when using low cost non-specialized digital cameras. Measurement traceability is set only by scale bars available in the measuring scene, avoiding the uncertainty contribution of off-process camera calibration procedures or the use of special purpose calibration artifacts. The developed self-calibrated in-process photogrammetry has been evaluated both in a pilot case scenario and in industrial scenarios for raw part measurement, showing a total in-process computing time typically below 1 s per image up to a maximum of 2 s during the last stages of the computed industrial scenes, along with a relative precision of 1/10,000 (e.g., 0.1 mm error in 1 m) with an error RMS below 0.2 pixels at image plane, ranging at the same performance reported for portable photogrammetry with precise off-process pre-calibrated cameras.
Highlights
Large parts machining is performed on a near-to-shape raw part, obtained by processes like casting or welding
Performance of the in-process computing procedure was observed, ranging at a total maximum in-process computing time of almost 2 s per image for the last images taken during the measuring processes, higher than the 1 s per image observed at the pilot case scenario, but still enabling practical quasi real time diagnosis and control of correct images taken in an industrial scenario, so that every time an incorrect images was acquired (due to an image not contributing to a consistent epipolar net, pending target to be solved in(c)a particular zone of the scene, etc.)
In-process bundle computing capability is demonstrated on a consumer grade desktop PC, enabling quasi real time 2D image and 3D scene computing and diagnosis so that a reliable measuring procedure can be conducted, avoiding inefficient user-dependent post-process iterative procedures that limit the potential of portable photogrammetry for an easy, low-cost and fast solution for industrial metrology of large components
Summary
Large parts machining is performed on a near-to-shape raw part, obtained by processes like casting or welding. These raw parts very often do not have any reliable surface or feature reference that can be used for in-machine alignment. Initial alignment of the part at the machine is a critical process, since an incorrect alignment will give rise to material shortage, which is associated with spoiling the part or with a costly recovery process. Due to the high cost associated with the rejection of a part, the initial alignment is usually done by way of long, time consuming manual processes.
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