Self-Calibrated In-Process Photogrammetry for Large Raw Part Measurement and Alignment before Machining

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.

Development and Verification of a Novel Measurement and Position System for Confined Cabin

“…In detail, the methods based on machine vision can be divided into three parts: photogrammetry, line-scan camera and structured light scanning. For example, Mendikute developed a self-calibrated in-process photogrammetry system [5]. The main innovations of this method lie in the optimized in-process joint bundle adjustment algorithm and the self-calibrated method for camera and lens distortion.…”

Diffuse retro-reflective imaging for improved video tracking of mosquitoes at human baited bednets

“…Field studies to examine mating behaviour have been reported using the setting sun as a back light with a pair of stereo cameras to give threedimensional mosquito tracks over volumes of metre-scale dimensions, but this imaging approach cannot be translated to the inside of dwellings in nocturnal situations [17][18][19]. Stereo or multi-camera three-dimensional imaging provides spatial resolution that increases proportionately with the field of view [20,21]. Another multi-camera approach (up to 11 cameras) for three-dimensional tracking flying animals was reported in [22].…”

Volumetric error modelling of a stereo vision system for error correction in photogrammetric three-dimensional coordinate metrology

“…By using a reference artefact of calibrated size and form, the stereo images of the object's surface are used for photogrammetric reconstruction. The compact nature of photogrammetric systems makes them flexible and integrable into machine tools [23], enhancing their in-situ and in-process applications [24]. Also, it has been shown that the surface texture of parts fabricated by additive manufacturing can be used as the source of disparity for feature extraction and matching [25].…”