• Smart Structures and Systems
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• v.20 no.4
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• pp.397-413
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• 2017

Precast concrete (PC) members are currently being employed for general construction or partial replacement to reduce construction period. As assembly work in PC construction requires connecting PC members accurately, measuring the 6-DOF (degree of freedom) relative displacement is essential. Multiple planar markers and camera-based displacement measurement systems can monitor the 6-DOF relative displacement of PC members. Conventional methods, such as direct linear transformation (DLT) for homography estimation, which are applied to calculate the 6-DOF relative displacement between the camera and marker, have several major problems. One of the problems is that when the marker is partially hidden, the DLT method cannot be applied to calculate the 6-DOF relative displacement. In addition, when the images of markers are blurred, error increases with the DLT method which is employed for its estimation. To solve these problems, a hybrid method, which combines the advantages of the DLT and MCL (Monte Carlo localization) methods, is proposed. The method evaluates the 6-DOF relative displacement more accurately compared to when either the DLT or MCL is used alone. Each subsystem captures an image of a marker and extracts its subpixel coordinates, and then the data are transferred to a main system via a wireless communication network. In the main system, the data from each subsystem are used for 3D visualization. Thereafter, the real-time movements of the PC members are displayed on a tablet PC. To prove the feasibility, the hybrid method is compared with the DLT method and MCL in real experiments.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.28-38
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• 2014

This paper verifies the performance of Extended Kalman Filter(EKF) and MCL(Monte Carlo Localization) approach to localization of an underwater vehicle through experiments. Especially, the experiments use acoustic range sensor whose measurement accuracy and uncertainty is not yet proved. Along with localization, the experiment also discloses the uncertainty features of the range measurement such as bias and variance. The proposed localization method rejects outlier range data and the experiment shows that outlier rejection improves localization performance. It is as expected that the proposed method doesn't yield as precise location as those methods which use high priced DVL(Doppler Velocity Log), IMU(Inertial Measurement Unit), and high accuracy range sensors. However, it is noticeable that the proposed method can achieve the accuracy which is affordable for correction of accumulated dead reckoning error, even though it uses only range data of low reliability and accuracy.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.357-361
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• 2007

This paper presents a multi-robot localization based on Bayesian Multidimensional Scaling (BMDS). We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nystr${\ddot{o}}$m approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. We not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.433-438
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• 2007

This paper presents a distance mapping-based localization method with incomplete data which means partially observed data. We make three contributions. First, we propose the use of Multi Dimensional Scaling (MDS) for multi-robot localization. Second, we formulate the problem to accomodate partial observations common in multi-robot settings. We solve the resulting optimization problem using #Scaling by Majorizing a Complicated function (SMACOF)#, a popular algorithm fur iterative MDS. Third, we not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.1
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• pp.148-156
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• 2011

This paper presents an implementation of autonomous navigation of a mobile robot indoors. It explains methods for map building, localization, obstacle avoidance and path planning. Geometric map is used for localization and path planning. The localization method calculates sensor data based on the map for comparison with the real sensor data. Monte Carlo Localization(MCL) method is adopted for estimation of the robot position. For obstacle avoidance, an artificial potential field generates repulsive and attractive force to the robot. Dijkstra algorithm plans the shortest distance path from a start position to a goal point. The methods integrate into autonomous navigation method and implemented for indoor navigation. The experiments show that the proposed method works well for safe autonomous navigation.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.345-353
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• 2010

A general solution in global self-position location of robot is to generate multiple hypothesis in self-position of robot, which is to look for the most positive self-position by evaluating each hypothesis based on features of observed landmark. Markov Localization(ML) or Monte Carlo Localization(MCL) to be the existing typical method is to evaluate all pairs of landmark features and generated hypotheses, it can be said to be an optimal method in sufficiently calculating resources. But calculating quantities was proportional to the number of pairs to evaluate in general, so calculating quantities was piled up in wide environments in the presence of multiple pairs if using these methods. First of all, the positive and promising pairs is located and evaluated to solve this problem in this paper, and the newly locating method to make effective use of calculating time is proposed. As the basic method, it is used both RANSAC(RANdom SAmple Consensus) algorithm and preemption scheme to be efficiency method of RANSAC algorithm. The calculating quantity on each observation of robot can be suppressed below a certain values in the proposed method, and the high location performance can be determined by an experimental on verification.