• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.4
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• pp.670-691
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• 2013

In RSSI-based RFID(Radio Frequency IDentification) indoor localization system, the signal path loss model of each sub-region is different from others in the whole localization area due to the influence of the multi-path phenomenon and other environmental factors. Therefore, this paper divides the localization area into many sub-regions and constructs separately the signal path loss model of each sub-region. Then an improved LANDMARC method is proposed. Firstly, the deployment principle of RFID readers and tags is presented for constructing localization sub-region. Secondly, the virtual reference tags are introduced to create a virtual signal strength space with RFID readers and real reference tags in every sub-region. Lastly, k nearest neighbor (KNN) algorithm is used to locate the target object and an error compensating algorithm is proposed for correcting localization result. The results in real application show that the new method enhances the positioning accuracy to 18.2% and reduces the time cost to 30% of the original LANDMARC method without additional tags and readers.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.995-1005
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• 2011

An efficient outdoor local localization method is suggested using multi-sensor fusion with MU-EKF (Multi-Update Extended Kalman Filter) for car-like mobile robots. In outdoor environments, where mobile robots are used for explorations or military services, accurate localization with multiple sensors is indispensable. In this paper, multi-sensor fusion outdoor local localization algorithm is proposed, which fuses sensor data from LRF (Laser Range Finder), Encoder, and GPS. First, encoder data is used for the prediction stage of MU-EKF. Then the LRF data obtained by scanning the environment is used to extract objects, and estimates the robot position and orientation by mapping with map objects, as the first update stage of MU-EKF. This estimation is finally fused with GPS as the second update stage of MU-EKF. This MU-EKF algorithm can also fuse more than three sensor data efficiently even with different sensor data sampling periods, and ensures high accuracy in localization. The validity of the proposed algorithm is revealed via experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.530-536
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• 2008

Localization is one of the fundamental problems in wireless sensor networks (WSNs) that forms the basis for many location-aware applications. Localization in WSNs is to determine the position of node based on the known positions of several nodes. Most of previous localization method use triangulation or multilateration based on the angle of arrival (AOA) or distance measurements. In this paper, we propose an enhanced centroid localization method based on edge weights of adjacent nodes using fuzzy modeling and genetic algorithm when node connectivities are known. The simulation results shows that our proposed centroid method is more accurate than the simple centroid method using connectivity only.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.111-118
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• 2010

The proposed algorithm to be explained in this paper is the localization technique using directional antenna. Here, it is assumed that anchor node has the ability to transfer the azimuth of each sector using GPS modules, sector antenna, and the digital compass. In the conventional sensor network, the majority of localization algorithms were capable of estimating the position information of the sensor node by knowing at least 3 position values of anchor nodes. However, this paper has proposed localization algorithm that estimates the position of nodes to continuously move with sensor nodes and traveling nodes. The proposed localization mechanisms have been simulated in the Matlab. The simulation results show that our scheme performed better than other mechanisms (e.g. MCL, DV-distance).

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.658-663
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• 2012

An accurate outdoor localization method using line/arc features is suggested for mobile robots with LRFs (Laser Range Finders) and odometry. Localization is a key process for outdoor mobile robots which are used for autonomous navigation, exploration and so on. In this paper, an accurate pose correction algorithm is proposed for mobile robots using LRFs, which use three feature types: line, circle, and arc. Using this method we can reduce the number of singular cases that robots couldn't find their pose. Finally we have got simulation results to validate the proposed algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.223-228
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• 2011

In this paper, a novel range-free localization algorithm for anisotropic networks is proposed. In the proposed method, the characteristics of the given network are considered and each sensor node estimates the relation between the hop counts and the geographical distances. Unlike most of the previous localization algorithms, the proposed method performs well not only in the isotropic network but also in the anisotropic networks. The proposed method is applied to both isotropic and anisotropic network topologies and the simulation results demonstrate that the method exhibits excellent and robust performances.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1884-1885
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• 2011

In the wireless sensor network (WSN) environment, the approximate point-in-triangulation (APIT) is a kind of range-free localization algorithm. This algorithm provides high precision, however, the coverage rate is somewhat poor. In this paper, we propose an improved APIT algorithm for the localization of swarm robots, which is based on the received signal strength indicator (RSSI) and the center of gravity (COG) methods.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.81-84
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• 2004

In this paper, we propose a virtual sound localization algorithm which improves the sound localization accuracy and sound color preservation for two channel and multi-channel surround speaker layouts. In conventional CPP laws, the sound direction is different from the panning angle and the sound color is different from real sound source especially when the speakers are spread out widely. To overcome this drawback, we design a virtual sound localization algorithm using directional psychoacoustic criteria (DPC) and sound color compensator (SCC). The analysis results show that in the case of the proposed system, the sound direction is the same as the panning angle in the audible frequency range and the sound color is less deviated from a real sound source than the conventional CPP law. In addition, its performance is verified by means of subjective tests using a real sound source.

• The Journal of Korea Robotics Society
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• v.2 no.1
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• pp.29-39
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• 2007

We present an implementation of particle filter algorithm for global localization and kidnap recovery of mobile robot. Firstly, we propose an algorithm for efficient particle initialization using sonar line features. And then, the average likelihood and entropy of normalized weights are used as a quality measure of pose estimation. Finally, we propose an active kidnap recovery by adding new particle set. New and independent particle set can be initialized by monitoring two quality measures. Added particle set can re-estimate the pose of kidnapped robot. Experimental results demonstrate the capability of our global localization and kidnap recovery algorithm.