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The stereometry of the working units of mining machines is optimized at the design stage, in terms of selected criteria based on computer simulations of the mining process. The recovered bodies of cutting heads or drums used in manufacturing are regenerated in the overhaul process. Ensuring that their dimensions comply with the nominal ones is labor-intensive and raises production costs. However, deviations of these components from the nominal shape can make it difficult to position the pick holders (which can cause collisions) or make welding them impossible (which results from too large a distance between the pick holders’ base and the side surface of the working unit). This applies especially to robotic technologies. By utilizing automatic (online) measurements of the distribution of the actual distances of the pick holders’ bases from the side surface of the working unit (taken during their positioning using a robot), it is possible to correct their positions without changing the setting of the pick itself. This measurement can be carried out using the non-contact stereovision method. This paper presents a method of matching raster images obtained from a stereovision system installed on an experimental robotic station. The results are presented of the numerical research carried out to select a mathematical model of the geometric transformation of images, for ensuring the effectiveness of the developed method.
The stereometry of the working units of mining machines is optimized at the design stage, in terms of selected criteria based on computer simulations of the mining process. The recovered bodies of cutting heads or drums used in manufacturing are regenerated in the overhaul process. Ensuring that their dimensions comply with the nominal ones is labor-intensive and raises production costs. However, deviations of these components from the nominal shape can make it difficult to position the pick holders (which can cause collisions) or make welding them impossible (which results from too large a distance between the pick holders’ base and the side surface of the working unit). This applies especially to robotic technologies. By utilizing automatic (online) measurements of the distribution of the actual distances of the pick holders’ bases from the side surface of the working unit (taken during their positioning using a robot), it is possible to correct their positions without changing the setting of the pick itself. This measurement can be carried out using the non-contact stereovision method. This paper presents a method of matching raster images obtained from a stereovision system installed on an experimental robotic station. The results are presented of the numerical research carried out to select a mathematical model of the geometric transformation of images, for ensuring the effectiveness of the developed method.
Regular inspections during construction work ensure that the completed work aligns with the plans and specifications and that it is within the planned time and budget. This requires frequent physical site observations to independently measure and verify the completion percentage of the construction progress performed over periods of time. The current computer vision techniques for measuring as-built elements predominantly employ three-dimensional laser scanning or three-dimensional photogrammetry modeling to ascertain the geometric properties of as-built elements on construction sites. Both techniques require data acquisition from several positions and angles to generate sufficient information about the element’s coordinates, making the deployment of these techniques on dynamic construction project sites challenging. This paper proposes a pipeline for automating the measurement of as-built components using artificial intelligence and computer vision techniques. The pipeline requires a single image obtained with a stereo camera system to measure the sizes of selected objects or as-built components. The results in this work were demonstrated by measuring the sizes of concrete walls and columns. The novelty of this work is attributed to the use of a single image and a single target for developing a fully automated computer vision-based method for measuring any given object. The proposed solution is suitable for use in measuring the sizes of as-built components in built assets. It has the potential to be further developed and integrated with building information modelling applications for use on construction projects for progress monitoring.
In this study, we propose a novel rectification method for three cameras using a single image for depth estimation. Stereo rectification serves as a fundamental preprocessing step for disparity estimation in stereoscopic cameras. However, off-the-shelf depth cameras often include an additional RGB camera for creating 3D point clouds. Existing rectification methods only align two cameras, necessitating an additional rectification and remapping process to align the third camera. Moreover, these methods require multiple reference checkerboard images for calibration and aim to minimize alignment errors, but often result in rotated images when there is significant misalignment between two cameras. In contrast, the proposed method simultaneously rectifies three cameras in a single shot without unnecessary rotation. To achieve this, we designed a lab environment with checkerboard settings and obtained multiple sample images from the cameras. The optimization function, designed specifically for rectification in stereo matching, enables the simultaneous alignment of all three cameras while ensuring performance comparable to traditional methods. Experimental results with real camera samples demonstrate the benefits of the proposed method and provide a detailed analysis of unnecessary rotations in the rectified images.
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