• Journal of Positioning, Navigation, and Timing
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• v.2 no.1
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• pp.81-89
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• 2013

Many of the current visual odometry algorithms suffer from some extreme limitations such as requiring a high amount of computation time, complex algorithms, and not working in urban environments. In this paper, we present an approach that can solve all the above problems using a single camera. Using a planar motion assumption and Ackermann's principle of motion, we construct the vehicle's motion model as a circular planar motion (2DOF). Then, we adopt a 1-point method to improve the Ransac algorithm and the relative motion estimation. In the Ransac algorithm, we use a 1-point method to generate the hypothesis and then adopt the Levenberg-Marquardt method to minimize the geometric error function and verify inliers. In motion estimation, we combine the 1-point method with a simple least-square minimization solution to handle cases in which only a few feature points are present. The 1-point method is the key to speed up our visual odometry application to real-time systems. Finally, a Bundle Adjustment algorithm is adopted to refine the pose estimation. The results on real datasets in urban dynamic environments demonstrate the effectiveness of our proposed algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.4
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• pp.295-301
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• 2016

The out-of-squareness is one of the error sources that affect the positioning accuracy of machine tools and coordinate measuring machines. Laser interferometer is widely used to measure the position and angular errors, and can measure the squareness using an optical square. However, the squareness measurement using the laser interferometer is difficult, as compared to other errors due to complicated optics setup and Abbe's error occurrence. The effect of out-of-squareness mainly appears at the face-diagonal of the movable plane. The diagonal displacements are also affected by the position dependent geometric errors. In this study, the squareness estimation techniques via diagonal displacement measurement using the laser interferometer without an optical square were proposed. For accurate estimation and measurement time reduction, the errors selected from proposed discriminant were measured. Discrepancy between the proposed technique with the laser interferometer (with an optical square) result was $0.6{\mu}rad$.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.2
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• pp.198-208
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• 2016

The painting process of large ships is an intense manual operation that typically comprises 9-12% of the total shipbuilding cost. Accordingly, shipbuilders need to estimate the required amount of anti-corrosive coatings and painting resources for inventory and cost control. This study aims to develop a software system which enables the shipbuilders to estimate paint area using existing 3D CAD ship structural models. The geometric information of the ships structure are extracted from the existing shipbuilding CAD/CAM system and used to create painting zones. After specifying the painting zones, users can generate the paint faces by clipping structural parts inside each zone. Finally, the paint resources may be obtained from the product of the paint areas and required paint thickness. Implementing the developed software system to real shipbuilders' operations has contributed to improved productivity, faster resource estimation, better accuracy, and fewer coating defects over their conventional manual calculation methods for painting resource estimation.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.24 no.1
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• pp.1-22
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• 2020

An accurate and efficient gradient estimation method on unstructured grid is presented by proposing a switching process between two Least-Square methods. Diverse test cases show that the gradient estimation by Least-Square methods exhibit better characteristics compared to Green-Gauss approach. Based on the investigation, switching between the two Least-Square methods, whose merit complements each other, is pursued. The condition number of the Least-Square matrix is adopted as the switching criterion, because it shows clear correlation with the gradient error, and it can be easily calculated from the geometric information of the grid. To illustrate switching process on general grid, condition number is analyzed using stencil vectors and trigonometric relations. Then, the threshold of switching criterion is established. Finally, the capability of Switching Weighted Least-Square method is demonstrated through various two- and three-dimensional applications.

• The Journal of Korea Robotics Society
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• v.12 no.1
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• pp.33-41
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• 2017

We present a region-based approach for accurate pose estimation of small mechanical components. Our algorithm consists of two key phases: Multi-view object co-segmentation and pose estimation. In the first phase, we explain an automatic method to extract binary masks of a target object captured from multiple viewpoints. For initialization, we assume the target object is bounded by the convex volume of interest defined by a few user inputs. The co-segmented target object shares the same geometric representation in space, and has distinctive color models from those of the backgrounds. In the second phase, we retrieve a 3D model instance with correct upright orientation, and estimate a relative pose of the object observed from images. Our energy function, combining region and boundary terms for the proposed measures, maximizes the overlapping regions and boundaries between the multi-view co-segmentations and projected masks of the reference model. Based on high-quality co-segmentations consistent across all different viewpoints, our final results are accurate model indices and pose parameters of the extracted object. We demonstrate the effectiveness of the proposed method using various examples.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1199-1209
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• 2021

The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

• Journal of KIISE
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• v.42 no.10
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• pp.1185-1194
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• 2015

A spherical panoramic image does not conform to the pin-hole camera model, and, hence, it is not possible to utilize previous techniques consisting of plane-to-plane transformation. In this paper, we propose a new method to estimate the planar geometric transformation between the planar image and a spherical panoramic image. Our proposed method estimates the transformation parameters for latitude, longitude, rotation and scaling factors when the matching pairs between a spherical panoramic image and a planar image are given. A planar image is projected into a spherical panoramic image through two steps of nonlinear coordinate transformations, which makes it difficult to compute the geometric transformation. The advantage of using our method is that we can uncover each of the implicit factors as well as the overall transformation. The experiment results show that our proposed method can achieve estimation errors of around 1% and is not affected by deformation factors, such as the latitude and rotation.

• Journal of Korean Society of Transportation
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• v.32 no.2
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• pp.170-178
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• 2014

Until now, several research on the relationship of traffic crash occurrences and geometric had been conducted and revealed that projects of road alignment, geometric improvement and hazardous segment selection reduced the number of accidents and accident severities. However, such variables did not consider the non-homogeneous characteristics of roadway segments due to the difficulty of data collection, which results in under-estimation of the standard error affecting the overall modeling goodness-of-fit. This study highlights the importance of non-homogeneity by looking at the effect of the non-homogeneous geometric variables through the modeling process. The model delivers meaningful results when using some geometric variables without relevant geometrics' variables.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.41-48
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• 2012

Using multi-sensor or multi-temporal high resolution satellite images together is essential for efficient applications in remote sensing area. The purpose of this paper is to estimate geometric difference of translations between high-resolution optical and SAR images automatically. The geometric and radiometric pre-processing steps were fulfilled to calculate the similarity between optical and SAR images by using Mutual Information method. The coarsest-level pyramid images of each sensor constructed by gaussian pyramid method were generated to estimate the initial translation difference of the x, y directions for calculation efficiency. The precise geometric difference of translations was able to be estimated by applying this method from coarsest-level pyramid image to original image in order. Yet even when considered only translation between optical and SAR images, the proposed method showed RMSE lower than 5m in all study sites.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.15-19
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• 2003

The Total Least Squares (TLS) method is introduced in comparison with the conventional Least Squares (LS) method. The principles and mathematical models for both methods are summarized and the comparison results from their applications to a simple geometric example, fitting a straight line to a set of 2D points are presented. As conceptually reasoned, the results clearly indicate that LS is more susceptible of producing wrong parameters with worse precision rather than TLS. For many applications in surveying, can adjustment computation and parameter estimation based on TLS provide better results.