The process and definition of camera calibration have change greatly over recent years. Aerial metric cameras calibration, for which laboratory and field calibration procedures were a separate process, was performed before and independent of any actual mapping data collection using precise calibration fixtures with an assumption that the camera parameters determined would remain valid for a significant period. In contrast, non-metric cameras are characterized by unstable intrinsic parameters over the times and they are vulnerable to the engine and other vibrations during flight data acquisitions. Moreover, there is no standard calibration procedures exist for these cameras. But, since non-metric camera self-calibration augments the concept of calibration as a part of the measurement process, it can determine the camera intrinsic parameters at the time of the data acquisition as long as highly-convergent geometry and the use of multiple exposures are employed. Therefore, this paper investigates variations of the lens distortion components with object distance within the photographic field by using the self-calibration method. The use of redundant flight paths and tilted camera is also incorporated to ascertain the stability of the principal distance and the principal points during the flight mission. During the experiments, a series of flight mission is conducted to photograph test field areas from over a relatively flat area to highly mountainous one. It is revealed that the radial, decentering, and affinity distortion parameters are more stable than that of the principal distance and principal points against vibrations.
Orthophoto maps are believed by mapping communities as a favorable media to generate land parcel boundaries for cadaster survey related projects. However, since burgeoning off-the-shelf cameras mounted on the unmanned aerial vehicle (UAV) are commonly utilized for photographing such boundaries, unreliable and unstable intrinsic parameters of these non-metric cameras impede good quality orthophoto productions. This paper presents an alternative method to measure the boundaries reliably without an existence of the orthophoto. A degraded quality of the orthophoto can be circumvented by our newly proposed method so called direct visual referencing. This method comprises two stages. The first step is to perform on the fly camera calibration to minimize instabilities of the intrinsic components of the non-metric camera. Modifying common and widely known flying paths for aerial photogrammetry mission is enabling a block variant self-calibrating bundle adjustments to proceed. The second step is a digitation process. Carefully selected Premark or prominent features along the boundaries are digitized on arbitrary selected images. These features are then matched to the similar ones onto all available images by performing multi photo geometrically constraint least squares image matching. Final results are 3D coordinates from the multi photos triangulation process. These boundaries coordinates are compared against the GPS-RTK measurements on the field. Deviations of these types of coordinates are around 1 cm. It is obvious that this method meets the precision requirement of the GPS-RTK measurements. Therefore we firmly believed that conducting UAV’s cadastral surveys using direct visual referencing is very promising in the near future.
Coplanarity-based relative orientation (RO) is one of the most crucial processes to obtain reliable 3D model and point clouds in Computer Vision and Photogrammetry community. Whilst a classical and rigorous procedure requires very close approximate values of five independent parameters, a direct method needs additional constraints to solve the parameters. This paper proposes a new approach that facilitates a very fast but stable and accurate solution from five point correspondences between two overlapping aerial images taken form unmanned aerial vehicle (UAV) flight. Furthermore, if 3D coordinates of perspective centers are available form geotagged images, rotational elements of the RO parameters can be quickly solved using three correspondences only. So it is very reliable for a provision of closed-form solutions for the rigorous methods. Our formulation regards Thompson’s parameterizations of Euler angles in composing a coplanarity condition. Nonlinear terms are subsequently added into a stereo parallax within a constant term under a linear least squares criteria. This strategy is considered new as compared with the known literatures since the proposed approach can find optimal solution. Results from real datasets confirm that our method produces a fast, stable and reliable linear solution by using at least five correspondences or even only three conjugate points of geotagged image pairs.
Pada umumnya objek jembatan merupakan infrastruktur transportasi yang memiliki konstruksi untuk dipantau secara periodik, salah satunya melalui monitoring deformasi. Dengan melihat beberapa teknologi yang berkembang untuk monitoring deformasi, diantaranya teknologi di bidang survei terestris seperti GPS, total station, dan waterpass yang pada dasarnya memiliki kelemahan tersendiri dari segi biaya dan waktu monitoring. Di bidang fotogrametri berkembang teknologi drone DJI Phantom 4 Pro yang dapat digunakan untuk monitoring deformasi. Penelitian ini berfokus untuk melakukan pengujian kemampuan pada teknologi drone DJI Phantom 4 Pro untuk pemantauan deformasi jembatan. Studi kasus deformasi berupa uji lendut jembatan dalam kondisi terbebani. Pengujian dilakukan dengan memanfaatkan teknik pemotretan konvergen pada akuisisi foto tanpa beban dan dengan beban. Proses data uji deformasi jembatan menggunakan metode bundle adjustment multi foto. Berdasarkan hasil pengujian teknologi drone untuk deformasi jembatan maka dapat mendeteksi lendutan dan pergeseran ke arah z positif yang relatif kecil (tidak terjadi kerusakan struktur) pada Jembatan Sambong dengan kisaran antara ±0,025 mm -1,281 mm serta ketelitian antara ±0,181 mm -0,773 mm. Berdasarkan tingkat ketelitian pergeseran tersebut, maka teknologi drone DJI Phantom 4 Pro mampu mendeteksi lendutan pada konstruksi jembatan hingga di bawah 1 mm.
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