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Photogrammetry is experiencing its renaissance in the 21st century. The main driving forces are research in computer vision as well as drone (UAV/UAS/RPAS) technology. There are popular open-source libraries and applications available in these fields (e.g. OpenCV and ODM). In this paper, we report our experiments using OpenCV in specific land surve...
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... ArUco markers are fixed to the moving structure, both methods can be used. In addition to the improvements of the algorithms, some hardware elements were also updated. We switched to Raspberry Pi model 3 and model 4 as well as to Pi Camera V2. A special adapter was developed to mount the camera on the eyepiece of an old basic Leica total station (Fig. 4.). Since only the telescope of the total station is used, there are no special requirements of the instrument. Beyond the small size and low energy consumption, its price made this instrument a preferable ...
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... ArUco markers are fixed to the moving structure, both methods can be used. In addition to the improvements of the algorithms, some hardware elements were also updated. We switched to Raspberry Pi model 3 and model 4 as well as to Pi Camera V2. A special adapter was developed to mount the camera on the eyepiece of an old basic Leica total station (Fig. 4.). Since only the telescope of the total station is used, there are no special requirements of the instrument. Beyond the small size and low energy consumption, its price made this instrument a preferable ...
Citations
... Special dictionaries are available for implementing these markers with 4 × 4, 5 × 5, 6 × 6, and 7 × 7 matrices. The optimal marker matrix resolutions and sizes vary subject to the target distance (Garrido-Jurado et al. 2016;Siki and Takács 2021). ArUco markers are binary square-based fiducial markers with fast detection and markers' ID capabilities. ...
... In case of the application of traditional GCPs/markers' practice on the construction site, the accuracy is dependent on the experience of site staff for measuring coordinates on GCPs/markers and error chances may increase with increasing number of GCPs/markers. However, one of the extremely vital concerns in the automation of close-range photogrammetric systems is the self-detection (automated) of coded targets in digital images (Mahami et al. 2019b), and manual ID of several GCPs or control point marking on images is a time-consuming activity (Siki and Takács 2021). Therefore, this paper aims to develop an effective, efficient marker-based scaling-up methodology for close-range photogrammetric systems by overcoming the mentioned concerns regarding automation and manual markings to implement digitalized construction progress monitoring. ...
... However, open-source software generally lacks automated marker detection in the images, which can be achieved by implementing Python-based solutions for a few open-source software for ArUco markers. Although paid photogrammetry tool versions provide automated markers detection as a built-in feature, the types of markers to use may vary (Siki and Takács 2021). Following the close-range photogrammetry criteria and available studies (Delgado-Vera et al. 2017;Qureshi et al. 2022b;Reljić et al. 2019), 12 photogrammetry software were short-listed, namely, VisualSFM, Meshroom, COLMAP, 3DF Zephyr, Regard 3D, RealityCapture, Autodesk ReCap Pro, Agisoft Metashape, PhotoModeler, MicMac, OpenMVG, and Multi-View Environment, which are most adopted by the research community. ...
Photogrammetry has gained the interest of professionals and researchers for activities related to construction projects' progress monitoring via attaining precise 3D point models. However, the precision of the generated models is directly linked with the precise scaling of the point cloud to ground truth dimensions (GTDs). Available scaling-up procedures for the close-range photogrammetry technique are complex, time consuming, and require human intervention, which adds the risk of error in the scaled-up model dimensions. Such a scenario creates hesitation among industry professionals toward implementing point cloud technologies. This paper devises an automated scaling-up methodology to overcome the said concerns by considering the construction progress monitoring theme. The intact process of automated scaling up of point cloud model to GTDs is controlled by two main parameters, that is, Python-based modules and designed ArUco-supported controlled markers. Remarkable outcomes are achieved with less than 1% scaled-up error compared with GTDs, which will improve the confidence of industry professionals toward point cloud technologies. Practical Applications: Photogrammetry applications have been adopted in several domains and the optimum usage of attained models can be executed with 3D replicas having precise details of surface features and geometry. Therefore, to attain 3D point cloud models with ground truth dimensions (GTDs), or actual dimensions of the targeted object the practitioners mostly follow the markers/ground control points (GCPs) technique (minimum three GCPs/markers), manual scaling, or georeferencing data. However, the accuracy of traditional GCPs/markers' technique and manual rescaling is dependent on the experience of the site staff/operator, and error chances may increase with the increasing number of GCPs/markers, whereas the georeferencing data-based technique is more technical and complex. Therefore, this paper developed an automated system for scaling up 3D point cloud models to GTDs with minimal human involvement. The system works with the help of specialized designed markers known as ArUco-supported controlled markers (ASCM). Only one ASCM marker is placed beside the targeted object for imaging; the devised system detects the marker in the images and rescales the developed point cloud model following the designed strategy. The system has high accuracy and can easily be implemented for scaling up close-range photogram-metry models in any domain.
... Basically, the aruco marker developed for camera pose estimation [12], but the researcher used this artificial marker for fruit size estimation. There is [29] 4×4, 5×5, 6×6, and 7×7 size markers and their available total number of possible ids are 50, 100, 250, and 1000 respectively. Download the aruco marker from its genuine website based on the requirement of the user shown in (Figure 4), make a printout of it, and paste it on a sheet where we are going to use it for size estimation. ...
Getting the size of any fruit on a tree is not an easy task especially mango fruit, because of its irregular shape, it is not easy to model with its shape. To do so we need the size of the fruit in length and width. In this journal, the researcher used the aruco marker for size estimation in computer vision for size recognition of the fruit, in image processing concepts, and got greater accuracy of the fruit size in real-time with good accuracy using an image processing and deep learning algorithm at detection. Objective: Horticulture farmers need to do some extra activities to get better yield like trying to know fruit shape, and fruit size at the time of maturity or before plucking fruits from the tree which will help farmers to get as per their predicted price while selling the fruits to the market nowadays. But the farmers are selling their fruits without knowing the size and shape of the fruit and their hard work because there is no measuring device to measure the farmer’s hard work, but there is a possibility to measure the size of the fruit which is a major drawback to them. To overcome this problem, the researchers tried to find a better solution for the farmers. Methods: Researchers applied a deep learning model named YOLOv7, Semantic Segmentation to get fruit size using an aruco marker. The researchers proposed a technique to help farmers that detect markers and the fruits of images and predict the size of the fruit at multi-targets. For this work, a custom dataset was created by collecting mango fruit frames from on-tree-mango-360° recorded video and the researcher did not augment the dataset. After training and validating this model, the performance was tested on the test dataset. Results: The contributions of this article are: The researcher developed a procedure to get the mango size from an image. The researcher implemented and tested a model to detect Banganapalle mango fruit in different challenging situations using YOLOv7 with Semantic Segmentation. Finally, the model achieved very good results on fruit size estimation. The training and testing results of YOLOv7-SS-AM show that the aruco marker-based model is superior to the manual size prediction, with good accuracy too.
... For UAV surveying application where photogrammetry techniques are used for products such as geo-referenced 3D models or orthophoto mosaics, having automatic detection of Ground Control Points (GCPs) is necessary in order to obtain accurate measurements and build reliable information. These methods mentioned above include basic techniques such as morphological operations, which are still used for GCPs detection [8], [9]. However, this does not allow the detection task to be scalable to different types of targets. ...
... This Collection includes 4 papers representing some of the latest pre-pandemic trends in the geospatial community. Luciani et al. (2021) present their contribution to the monitoring of environmental conditions using a FOSS4G-based workflow for satellite monitoring system of subalpine lakes implemented within the SIMILE project; Stoppe and Flenker (2021) present a unique and innovative application of FOSS4G tools, applying their principles to the case of designing and visualizing electronic printed circuit boards, facilitating smoother and more ubiquitous user experience; Takács and Siki (2021) look into the precision of local coordinate reference systems, required for even tighter integration of the GIS and surveying disciplines; Siki and Takács (2021) showcase the potential of free software-based solutions in the automatization of surveying by detecting and identifying control points for aerial photography and real-time building deformation monitoring. Together, these papers highlight issues that will probably remain high on the agenda of the FOSS4G community in the coming years, e.g., extending the capabilities and applications of free GIS tools, demand for even higher precision of location data, and automatic high precision monitoring of environmental conditions. ...
This is the foreword to the Special Issue in the Baltic Journal of Modern Computing, which includes the papers originally submitted to the Academic Track of the FOSS4G Europe 2020 conference. The conference was canceled due to the COVID-19 restrictions, but the Scientific Committee responsible for the Academic Track still managed to publish the Special Issue (available at https://www.bjmc.lu.lv/en/contents/vol-92021-no-1) as initially planned.
Purpose. The study aims to experimentally evaluate the effectiveness of using visual cues, namely ARToolKitPlus, ArUco markers and two-dimensional QR (quick response code) codes in the tasks of localizing unmanned vehicles (UVs) indoors. Methodology. To enable the implementation of the processes of localization and perception of unmanned vehicles based on visual marks (markers), the structure of the visual marks processing subsystem has been developed. An algorithm for the combined use of three types of visual markers – ARToolKitPlus, ArUco markers and QR codes – for localizing unmanned vehicles and identifying cargo is proposed using the example of an online store warehouse scenario. To conduct experiments on the markers, we chose a hardware and software tool such as the JeVois-A33 smart machine vision camera with the JeVois Markers Combo software module and the JeVois Inventor graphical interface. Findings. An experimental study of the possibility of correct recognition of visual marks for indoor working conditions was carried out. As a result of a series of experiments, the possibilities of correct recognition of visual marks such as ArUco, ARToolKitPlus markers and QR codes during scanning at right angles to the camera at a distance ranging from 0.3 to 2 meters were determined. Originality. The study obtained the probabilities of correct recognition of ArUco, ARToolKitPlus markers and two-dimensional QR codes in the conditions of localization of unmanned vehicles indoors. Practical value. The obtained results of the study can be used to create, simulate, and analyze the effectiveness of algorithms for localizing and perceiving unmanned vehicles indoors using appropriate visual markers. The proposed generalized structure of the visual marker processing subsystem of the localization and perception system can be used in the development of unmanned vehicle control systems.
