• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.1046-1052
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• 2016

For the safe driving of autonomous vehicles, accurate position estimation is required. Generally, position error must be less than 1m because of lane keeping. However, GPS positioning error is more than 1m. Therefore, we must correct this error and a map matching algorithm is generally used. Especially, road marking intensity map have been used in many studies. In previous work, 3D LIDAR with many vertical layers was used to generate a local intensity map. Because it can be obtained sufficient longitudinal information for map matching. However, it is expensive and sufficient road marking information cannot be obtained in rush hour situations. In this paper, we propose a localization algorithm using an accumulated intensity local map. An accumulated intensity local map can be generated with sufficient longitudinal information using 3D LIDAR with a few vertical layers. Using this algorithm, we can also obtain sufficient intensity information in rush hour situations. Thus, it is possible to increase the reliability of the map matching and get accurate position estimation result. In the experimental result, the lateral RMS position error is about 0.12m and the longitudinal RMS error is about 0.19m.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.523-531
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• 2010

A dam is effective in stable supply of water required in daily life and reduced damage from floods, but there are problems as a lot of land or houses are submerged. Therefore many projects have been conducted in order to improve and support daily life environment surrounding a dam. This study has focused on analyzing how to calculate support working expenses for Dam area by using GIS spatial overlay in addition to effects of scale of a topographic map and reached the following conclusion. First, as a result of areal error in submerged area by scale based on a 1/3,000 digital topographic map, it has been found that a 1/5,000 digital topographic map is 9.5 times more accurate than a 1/25,000 digital topographic map in the total of areal error. Second, as a result of areal error in area surrounding a dam, it has been found that a 1/5,000 digital topographic map is 7.4 times more accurate than a 1/25,000 digital topographic map in the total of areal error. Third, as a result of error of support expense for submerged area, it has been found that a 1/5,000 digital topographic is 15.9 times, 14.7 times and 15.9 times more effective than a 1/25,000 digital topographic map in terms of the total error of support expense, standard error and the total support expense error on the entire project costs in submerged area. In addition, as a result of analysis on error of support expense for area surrounding a dam, it has been found that a 1/5,000 digital topographic map was 10.7 times, 9.6 times and 10.6 times more effective, respectively, in the total error of support expense, standard error and the total error of support expense for the entire project costs in area surrounding a dam compared to a 1/25,000 digital topographic map. Lastly, as a result of error of the entire project costs for area surrounding a dam, it has been found that a 1/5,000 digital topographic map was 1.4 times, 1.3 times and 1.4 times more effective, respectively, in the total error of support expense, standard error and the total error of the entire project costs compared to a 1/25,000 digital topographic map, but it was not much different from the result of calculating areal error in submerged area or area surrounding a dam because population item didn't consider areal concept.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.25-31
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• 2022

Moving among multiple floors is one of the most challenging tasks for indoor autonomous robots. Most of the previous researches for indoor mapping and localization have focused on singular floor environment. In this paper, we present an algorithm that creates a multi-floor map using 3D point cloud. We implement localization within the multi-floor map using a LiDAR and an IMU. Our algorithm builds a multi-floor map by constructing a single-floor map using a LOAM-based algorithm, and stacking them through global registration that aligns the common sections in the map of each floor. The localization in the multi-floor map was performed by adding the height information to the NDT (Normal Distribution Transform)-based registration method. The mean error of the multi-floor map showed 0.29 m and 0.43 m errors in the x, and y-axis, respectively. In addition, the mean error of yaw was 1.00°, and the error rate of height was 0.063. The real-world test for localization was performed on the third floor. It showed the mean square error of 0.116 m, and the average differential time of 0.01 sec. This study will be able to help indoor autonomous robots to operate on multiple floors.

• Korean Journal of Optics and Photonics
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• v.22 no.3
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• pp.129-133
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• 2011

Dual-wavelength holography has better axial range than single-wavelength holography, allowing unambiguous phase imaging but at the expense of increased noise. We have studied error reduction in dual wavelength holography using a modified fine map. The fine map is successful in measurement and has shown error reduction when the height of the object is less than the appled wavelength, but is unstable when the step height is larger than that wavelength. We have modified the fine map and we have found that the modified fine map was successful in measurement and error reduction even though the height of object was larger than the wavelength.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.1037-1038
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• 2009

• Transactions of Materials Processing
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• v.13 no.4
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• pp.334-341
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• 2004

In this study, a remeshing algorithm adapted to the mesh density map using the Delaunay mesh generation method is developed. In the finite element simulation of forging process, the numerical error increases as the process goes on because of discrete property of the finite elements and distortion of elements. Especially, in the region where stresses and strains are concentrated, the numerical error will be highly increased. However, it is not desirable to use a uniformly fine mesh in the whole domain. Therefore, it is necessary to reduce the analysis error by constructing locally refined mesh at the region where the error is concentrated such as at the die corner. In this paper, the point insertion algorithm is used and the mesh size is controlled by using a mesh density map constructed with a posteriori error estimation. An optimized smoothing technique is adopted to have smooth distribution of the mesh and improve the mesh element quality.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.1
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• pp.1-7
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• 2021

In this paper, we presented the experimental basis for the theoretical background and robustness of the Convolutional Neural Network for object recognition based on artificial intelligence. An experimental result was performed to visualize the weighting filters and feature maps for each layer to determine what characteristics CNN is automatically generating. experimental results were presented on the trend of learning error and identification error rate by checking the relevance of the weight filter and feature map for learning error and identification error. The weighting filter and characteristic map are presented as experimental results. The automatically generated characteristic quantities presented the results of error rates for moving and rotating robustness to geometric changes.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.544-547
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• 1997

In this paper, we propose a dynamic localization method using a rotating sonar and a map. The proposed method is implemented by using extended Kalman filter. The state equation is based on the encoder propagation model and the encoder error model, and the measurement equation is a map-based measurement equation using a rotating sonar sensor. By utilizing sonar beam characteristics, map-based measurements are updated while AMR is moving continuously. By modeling and estimating systematic errors of a differential encoder, the position is successfully estimated even the interval of the map-based measurement. Monte-Carlo simulation shows that the proposed global position estimator has the performance of a few millimeter order in position error and of a few tenth degrees in heading error and of compensating systematic errors of the differential encoder well.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.3
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• pp.945-959
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• 1996

In this paper, a comprehensive computer model is described which can be used to generate the volumetric error map combining the machine parametric errors and the measurement prove error, for most types of CMMs and axis configurations currently in use.

• The Journal of Korea Robotics Society
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• v.9 no.4
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• pp.242-249
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• 2014

By a SLAM (simultaneous localization and mapping) method, we get a map of an environment for autonomous navigation of a robot. In this case, we want to know how accurate the map is. Or we want to know which map is more accurate when different maps can be obtained by different SLAM methods. So, several methods for map comparison have been studied, but they have their own drawbacks. In this paper, we propose a new method which compares the accuracy or error of maps relatively and quantitatively. This method sets many corresponding points on both reference map and SLAM map, and computes the translational and rotational values of all corresponding points using least-squares solution. Analyzing the standard deviations of all translational and rotational values, we can know the error of two maps. This method can consider both local and global errors while other methods can deal with one of them, and this is verified by a series of simulations and real world experiments.