• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3657-3682
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• 2018

This paper presents a Bluetooth and pedestrian dead reckoning (PDR)-based indoor fusion localization approach (BtPDR) using smartphones. A Bluetooth and PDR-based indoor fusion localization approach can localize the initial position of a smartphone with the received signal strength (RSS) of Bluetooth. While a smartphone is moving, BtPDR can track its position by fusing the localization results of PDR and Bluetooth RSS. In addition, BtPDR can adaptively modify the parameters of PDR. The contributions of BtPDR include: a Bluetooth RSS-based Probabilistic Voting (BRPV) localization mechanism, a probabilistic voting-based Bluetooth RSS and PDR fusion method, and a heuristic search approach for reducing the complexity of BRPV. The experiment results in a real scene show that the average positioning error is < 2m, which is considered adequate for indoor location-based service applications. Moreover, compared to the traditional PDR method, BtPDR improves the location accuracy by 42.6%, on average. Compared to state-of-the-art Wireless Local Area Network (WLAN) fingerprint + PDR-based fusion indoor localization approaches, BtPDR has better positioning accuracy and does not need the same offline workload as a fingerprint algorithm.

• 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.

• Journal of IKEEE
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• v.17 no.4
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• pp.440-445
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• 2013

This paper proposes a local estimation system which combines Cell Divide Algorithm with low-cost GPS/INS fused by Extended Kalman Filter(EKF) for localization of Quad-rotor when it returns to the departure point. In the research, the low-cost GPS and INS are fused by EKF to reduce the local error of low-cost GPS and the accumulative error of INS due to continuous integration of sensor error values. When the Quad-rotor returns to the departure point in the fastest path, a moving path can be known because it moves straight, where Cell Divide Algorithm is used to divide moving route into the cells. Then it determines the closest position of data of GPS/INS system fused by EKF to obtain the improved local data. The proposed system was verified through comparing experimental localization results obtained by using GPS, GPS/INS and GPS/INS with Cell Divide Algorithm respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.647-658
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• 2010

Many localization schemes estimate the locations of radio nodes based on the physical locations of anchors and the connectivity from the anchors. Since they only consider the knowledge of the anchors without else other nodes, they are likely to have enormous error in location estimate unless the range information from the anchors is accurate or there are sufficiently many anchors. In this paper, we propose a novel localization algorithm with the location knowledge of anchors and even one-hop neighbors to localize unknown nodes in the uniform distance from all the one-hop neighbors without the range information. The node in the uniform distance to its all neighbors reduces the location error relative to the neighbors. It further alleviates the location error between its actual and estimated locations. We evaluate our algorithm through extensive simulations under a variety of node densities and anchor placement methods.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.269-277
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• 2007

Wireless sensor network system is expected to get high attention in research for now and future owing to the advanced hardware development technology and its various applicabilities. Among variety of sensor network systems, the seashore and marine sensor network, which are extended to get sampling of marine resources, environmental monitoring to prevent disaster and to be applied to the area of sea route guidance. For these marine applications to be available, however, the provision of precise location information of every sensor nodes is essential. In this paper, the sequential localization algorithm for obtaining the location information of marine sensor nodes. The sequential localization is done with the utilization of a small number of beacon nodes along the seashore and gets the location of nodes by controling the sequences of localization and also minimizes the error accumulation. The key idea of this algorithm for localization is that the localization priority of each sensor nodes is determined by the number of reference nodes' information. This sequential algorithm shows the improved error performance and also provide the increased coverage of marine sensor network by enabling the maximum localization of sensor nodes as possible.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.155.1-155
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• 2001

The main contribution of this research is that it gives:(1) a rational criterion to select the best self-localizing method for a particular situation, and (2) an appropriate arrangement of the landmarks to minimize the error. In this paper, the authors propose a set of indices to evaluate the accuracy of the self-localizing methods, and the indices are derived from a sensitivity which is defined as the ratio of the localizing error to sensor error. And then, we compare the accuracy of self-localizing a mobile robot with landmarks based on the indices, and propose a rational way to minimize the localizing error.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.229-230
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• 2012

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.361-368
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• 2015

This paper reports an unscented Kalman filter approach for localization of a mobile robot in an indoor environment. The method proposes a new model of measurement uncertainty which adjusts the error covariance according to the measured distance. The method also uses non-zero off diagonal values in error covariance matrices of motion uncertainty and measurement uncertainty. The method is tested through experiments in an indoor environment of 100*40 m working space using a differential drive robot which uses Laser range finder as an exteroceptive sensor. The results compare the localization performance of the proposed method with the conventional method which doesn't use adaptive measurement uncertainty model. Also, the experiment verifies the improvement due to non-zero off diagonal elements in covariance matrices. This paper contributes to implementing and evaluating a practical UKF approach for mobile robot localization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8760-8766
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• 2015

In this paper a technology which gives directions to people and also localization of the robotic vacuum cleaners inside some spacious buildings is developed. For this purpose, it is confirmed that which pattern has a small error in dealing with the indoor localization with various RFID tag arrangements attached on the ground. This experiment was conducted by using MT92(900MHz range Antenna) and ALR 9900+(Reader). As a result, the square arrangement has the least error, 21.19cm, among other patterns which are diamond, rectangle and regular hexagon. However, it is necessary to consider the number of tags in the unit area, from this point of view the regular hexagon arrangement is the most efficient arrangement among other patterns because it needs only 6 tags in the unit area.

• 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.