• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.110-117
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• 2022

This paper describes a localization algorithm utilizing relative observations for multiple autonomous underwater vehicles (Multiple-AUVs). In order to maximize the efficiency of operation and mission accomplishment and to prevent problems such as collision and interference, the locations and directions of Multiple-AUVs must be precisely estimated. To estimate the locations and directions, we designed a localization algorithm utilizing relative observations and verified it with simulations based on sensor data sets acquired through real sea experiments. Also, an optimal combination of relative observation information for efficient localization is figured out through combining various relative observations. The proposed method shows improved localization results compared to those only using the navigation algorithm. The performance of localization is improved up to 58% depending on the combination of relative observations.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.2
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• pp.187-194
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• 2015

Accurate relative location estimation is a key requirement in indoor localization systems based on wireless sensor networks (WSNs). However, although these systems have applied not only various optimization algorithms but also fusion with sensors to achieve high accuracy in position determination, they are difficult to provide accurate relative azimuth and locations to users because of cumulative errors in inertial sensors with time and the influence of external magnetic fields. This paper based on ultra-wideband positioning system, which is relatively suitable for indoor localization compared to other wireless communications, presents an indoor localization system for estimating relative azimuth and location of location-unaware nodes, referred to as target nodes without applying any algorithms with complex variable and constraints to achieve high accuracy. In the proposed method, the target nodes comprising three mobile nodes estimate the relative distance and azimuth from two reference nodes that can be installed by users. In addition, in the process of estimating the relative localization information acquired from the reference nodes, positioning errors are minimized through a novel modified rounding estimation technique in which Kalman filter is applied without any time consumption algorithms. Experimental results show the feasibility and validity of the proposed system.

• Korean Journal of Cognitive Science
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• v.15 no.3
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• pp.15-24
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• 2004

The perceived position of a flashing target object is generally biased towards the direction of eye movement when there is no reference around the target. Current research examined the localization accuracy of a flashing target relative to a static reference. The perceived location of the target relative to the reference was distorted and the pattern of perceptual distortion systematically depended on the position of the reference relative to the target. This kind of result was consistently observed regardless of the distance between the reference and the target and direction of pursuit eye movement. We have discussed how these results could he explained by the theories previously suggested to explain the localization of objects.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.430-435
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• 2011

In this paper, a navigation technology of an unmanned vehicle using relative localization and magnetic guidance is proposed. Magnetic guidance system had been developed as a robust autonomous driving technology as long as magnetic fields on the path are detected. Otherwise, if magnetic fields were not detected due to some reasons, the vehicle could not drive. Therefore, in order to overcome the drawback, we propose that relative localization would be combined to magnetic guidance system. To validate the usefulness of the proposed method, a robotic vehicle was set up with the magnetic guidance system and the relative localization. In addition, the unmanned driving test was realized on the road without the magnetic fields so that the proposed method is verified by the experiment.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.5
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• pp.566-572
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• 2014

Recently, indoor navigation has been applied in large convention centers by using wireless sensor networks (WSNs), which provide not only a user's path to be traveled but also orientation and shopping information to increase user's convenience. This paper presents the localization system for estimating relative coordinates without pre-deployment of the reference node based on ultra wide band (UWB) ranging system, which is relatively suitable for indoor localization compared to other wireless communications, and azimuth sensor. The proposed localization system which consists of an azimuth sensor and a mobile node composed of three nodes estimates relative coordinates of the reference node without applying any recursive and time consumption algorithms. Also, in the process of estimating relative coordinates of the reference node, ranging errors are minimized through the proposed technique and the number of nodes can be reduced. Experimental results show the feasibility and validity of the proposed system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.173-180
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• 2006

A key component of autonomous navigation of intelligent home robot is localization and map building with recognized features from the environment. To validate this, accurate measurement of relative location between robot and features is essential. In this paper, we proposed relative localization algorithm based on 3D reconstruction of scale invariant features of two images which are captured from two parallel cameras. We captured two images from parallel cameras which are attached in front of robot and detect scale invariant features in each image using SIFT(scale invariant feature transform). Then, we performed matching for the two image's feature points and got the relative location using 3D reconstruction for the matched points. Stereo camera needs high precision of two camera's extrinsic and matching pixels in two camera image. Because we used two cameras which are different from stereo camera and scale invariant feature point and it's easy to setup the extrinsic parameter. Furthermore, 3D reconstruction does not need any other sensor. And the results can be simultaneously used by obstacle avoidance, map building and localization. We set 20cm the distance between two camera and capture the 3frames per second. The experimental results show :t6cm maximum error in the range of less than 2m and ${\pm}15cm$ maximum error in the range of between 2m and 4m.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.8
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• pp.678-687
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• 2005

This paper describes a relative localization algorithm using odometry data and consecutive local maps. The purpose of this paper is the odometry error correction using the area matching of two consecutive local maps. The local map is built up using a sensor module with dual laser beams and USB camera. The range data form the sensor module is measured using the structured lighting technique (active stereo method). The advantage in using the sensor module is to be able to get a local map at once within the camera view angle. With this advantage, we propose the AVS (Aligned View Sector) matching algorithm for. correction of the pose error (translational and rotational error). In order to evaluate the proposed algorithm, experiments are performed in real environment.

• The Journal of Korea Robotics Society
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• v.11 no.1
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• pp.9-18
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• 2016

A rectangle-based relative localization method is proposed for a mobile robot based on a novel geometric formulation. In an artificial environment where a mobile robot navigates, rectangular shapes are ubiquitous. When a scene rectangle is captured using a camera attached to a mobile robot, localization can be performed and described in the relative coordinates of the scene rectangle. Especially, our method works with a single image for a scene rectangle whose aspect ratio is not known. Moreover, a camera calibration is unnecessary with an assumption of the pinhole camera model. The proposed method is largely based on the theory of coupled line cameras (CLC), which provides a basis for efficient computation with analytic solutions and intuitive geometric interpretation. We introduce the fundamentals of CLC and describe the proposed method with some experimental results in simulation environment.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.4
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• pp.508-516
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• 2016

When GPS signals are not available, a relative localization can be alternatively used to represent the topological relationship between mobile nodes. A relative location map of a network can be constructed by using the distance information between all the pairs of nodes in the network. If a network is large, a number of small local maps are individually constructed and are merged to obtain the whole map. However, this approach may result in a high computation and communication overhead. In this paper, we propose a reference-node selection scheme for relative localization map construction, which chooses a subset of nodes as a reference node that is supposed to construct local maps. The scheme is a greedy algorithm that iteratively chooses nodes with high degree as a reference node until the chosen local maps are successfully merged with a sufficient number of common nodes between nearby local maps. The simulation results indicate that the proposed scheme achieves higher localization accuracy with a reduced computational overhead.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.86-88
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• 2005

An autonomous semantic-map building method is proposed, with the robot localized in the semantic-map. Our semantic-map is organized by objects represented as SIFT features and vision-based relative localization is employed as a process model to implement extended Kalman filters. Thus, we expect that robust SLAM performance can be obtained even under poor conditions in which localization cannot be achieved by classical odometry-based SLAM