Scanning Photogrammetry for Measuring Large Targets in Close Range

Abstract

Abstract:In close-range photogrammetry, images are difficult to acquire and organize primarily because of the limited field of view (FOV) of digital cameras when long focal lenses are used to measure large targets. To overcome this problem, we apply a scanning photography method that acquires images by rotating the camera in both horizontal and vertical directions at one station. This approach not only enlarges the FOV of each station but also ensures that all stations are distributed in order without coverage gap. We also conduct a modified triangulation according to the traits of the data overlapping among images from the same station to avoid matching all images with one another. This algorithm synthesizes the images acquired from the same station into synthetic images, which are then used to generate a free network. Consequently, we solve the exterior orientation elements of each original camera image in the free network and perform image matching among original images to obtain tie points. Finally, all original images are combined in self-calibration bundle adjustment with control points. The feasibility and precision of the proposed method are validated by testing it on two fields using 300 and 600 mm lenses. The results confirm that even with a small amount of control points, the developed scanning photogrammetry can steadily achieve millimeter scale accuracy at distances ranging from 40 m to 250 m.

Extraction of Sample Plot Parameters from 3D Point Cloud Reconstruction Based on Combined RTK and CCD Continuous Photography

“…In the same year, Liang et al [26] developed uncelebrated hand-held consumer cameras for terrestrial photogrammetry with 88% data accuracy. Huang et al [27] developed a photoelectric scanner consisting of a non-measurement camera, a rotating platform, and a controller. With the continuous development of imaging technology, there are many commercial software programs based on an image point cloud such as Smart3D (Acute3D-A Bentley Systems Company, Paris, French), Pix4D mapper (Pix4D company, Lausanne, Switzerland) and Agisoft PhotoScan (AgiSoft LLC, Petersburg, Russia) [26,[28][29][30].…”

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

“…Other than assuming nominal values for intrinsic parameters, precise camera calibration is required for precise measuring. Accurate camera calibration is necessary in photogrammetry systems and that is why continuous and recent improvements regarding mathematical analysis of the data taken for self-calibration [ 11 , 12 , 13 ] and image distortion correction [ 14 , 15 ] can be found in the literature. Nowadays there are several software applications for close range photogrammetry in industry using optical targets (i.e., Aicon 3D, Geodetic V-Stars, Creaform MaxShot3D, GOM Tritop, etc.)…”

“…Indeed, camera calibration is a relevant uncertainty contributor in photogrammetry [ 2 , 10 , 11 ], and precise measurements require precise camera model calibration (so called intrinsic parameters). Continuous and recent improvements regarding the mathematical analysis of the data taken for calibration [ 11 , 12 , 13 ] and image distortion correction [ 14 , 15 ] can be found in the literature.…”

Assessment of the Precision of a Smart-Phone Pole Photogrammetry for a Second-Order Cliff Surface Deformation Studies

“…The distance between these ground-based stations and the cliff surface (L) is as the height (H) above the ground in aerial surveys. Like the traditional aerial surveys, camera-based stations on the ground would require very careful and systematic planning to achieve the best Ground Sampling Distance (GSD) (Huang et al, 2015), sensor stability (Roncella et al, 2014), overlap and reduce the effects of random errors and accumulation of systematic errors (Aber and Babb, 2018;James and Robson, 2014). The length between the baseline and cliff (L), the linear dimension of the pixel (px), and the camera lens' focal length (f) will determine GSD accuracies as camera pixels will be mapped to the cliff surface Geological formations of limestone, sandstone, chalk, rock shelters, and granite characterize the cliff surface (Terefenko et al, 2018).…”