• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2094-2102
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• 2016

In this paper, we propose an extended Kalman filter(EKF)-based simultaneous localization and mapping(SLAM) method using laser corner pattern matching for mobile robots. SLAM is one of the most important problems of mobile robot. However, existing method has the disadvantage of increasing the computation time, depending on the number of landmarks. To improve computation time, we produce the corner pattern using classified and detected corner points. After producing the corner patterns, it is estimated that mobile robot's global position by matching them. The estimated position is used as measurement model in the EKF. To evaluated proposed method, we preformed the experiments in the indoor environments. Experimental results of proposed method are shown to maintain an accuracy and decrease the computation time.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.3
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• pp.215-220
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• 2024

In urban areas, signals can be blocked and reflected by buildings, reducing the reliability of global navigation satellite systems (GNSS). To address this, the zonotope shadow matching (ZSM) algorithm has been proposed to estimate the set-valued receiver position by calculating the GNSS shadow based on the zonotope. However, the existing study only analyzed the performance of ZSM in dense urban areas where GNSS shadows occur frequently, and the performance analysis in various urban environments was insufficient. Therefore, in this paper, we analyzed the performance of the ZSM algorithm in four urban environments with different characteristics. The results showed that the receiver position estimation performance of ZSM was relatively poor in environments where buildings were not densely populated, and the performance of ZSM was shown to be effective in urban environments with narrow roads and tall buildings.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.33-39
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• 2020

This paper presents an Around View Monitoring (AVM) stop-line detection based longitudinal position correction algorithm for automated driving on urban roads. Poor positioning accuracy of low-cost GPS has many problems for precise path tracking. Therefore, this study aims to improve the longitudinal positioning accuracy of low-cost GPS. The algorithm has three main processes. The first process is a stop-line detection. In this process, the stop-line is detected using Hough Transform from the AVM camera. The second process is a map matching. In the map matching process, to find the corrected vehicle position, the detected line is matched to the stop-line of the HD map using the Iterative Closest Point (ICP) method. Third, longitudinal position of low-cost GPS is updated using a corrected vehicle position with Kalman Filter. The proposed algorithm is implemented in the Robot Operating System (ROS) environment and verified on the actual urban road driving data. Compared to low-cost GPS only, Test results show the longitudinal localization performance was improved.

• ETRI Journal
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• v.32 no.6
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• pp.891-900
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• 2010

In this paper, a map-matching (MM) algorithm which combines an estimated position with digital road data is proposed. The presented algorithm using a virtual track is appropriate for a MEMS-based pedestrian dead reckoning (PDR) system, which can be used in mobile devices. Most of the previous MM algorithms are for car navigation systems and GPS-based navigation system, so existing MM algorithms are not appropriate for the pure DR-based pedestrian navigation system. The biggest problem of previous MM algorithms is that they cannot determine the correct road segment (link) due to the DR characteristics. In DR-based navigation system, the current position is propagated from the previous estimated position. This means that the MM result can be placed on a wrong link when MM algorithm fails to decide the correct link at once. It is a critical problem. Previous algorithms never overcome this problem because they did not consider pure DR characteristics. The MM algorithm using the virtual track is proposed to overcome this problem with improved accuracy. Performance of the proposed MM algorithm was verified by experiments.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.507-510
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• 2002

An algorithm of 3-D particle image velocimetry(3D-PIV) was developed for the measurement of 3-D velocity Held of complex flows. The measurement system consists of two or three CCD camera and one RGB image grabber. Flows size is $1500{\times}100{\times}180(mm)$, particle is Nylon12(1mm) and illuminator is Hollogen type lamp(100w). The stereo photogrammetry is adopted for the three dimensional geometrical mesurement of tracer particle. For the stereo-pair matching, the camera parameters should be decide in advance by a camera calibration. Camera parameter calculation equation is collinearity equation. In order to calculate the particle 3-D position based on the stereo photograrnrnetry, the eleven parameters of each camera should be obtained by the calibration of the camera. Epipolar line is used for stereo pair matching. The 3-D position of particle is calculated from the three camera parameters, centers of projection of the three cameras, and photographic coordinates of a particle, which is based on the collinear condition. To find velocity vector used 3-D position data of the first frame and the second frame. To extract error vector applied continuity equation. This study developed of various 3D-PIV animation technique.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.159-162
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• 2002

This paper presents a modified localization scheme of a mobile robot. When it navigates, the position error of a robot is increased and doesn't go to a goal point where the robot intends to go at the beginning. The objective of localization is to estimate the position of a robot precisely. Many algorithms were developed and still are being researched for localization of a mobile robot at present. Among them, a localization algorithm named continuous localization proposed by Schultz has some merits on real-time navigation and is easy to be implemented compared to other localization schemes. Continuous Localization (CL) is based on map-matching algorithm with global and local maps using only ultrasonic sensors for making grid maps. However, CL has some problems in the process of searching the best-scored-map, when it is applied to a mobile robot. We here propose fast and powerful map-matching algorithm for localization of a mobile robot by experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.38.4-38
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• 2002

We propose a fast map-matching algorithm based on the length and the slope for the sequence of lines extracted from a laser range finder and a map. After finding two feature set from laser data and a map, the position and heading of the mobile robot can be determined exactly.

• Journal of Astronomy and Space Sciences
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• v.15 no.1
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• pp.229-234
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• 1998

We study on a terrain contour matching algorithm using Radial Basis Functions(RBFs) for aided inertial navigation system for position fixing aircraft, cruise missiles or re-entry vehicles. The parameter optimization technique is used for updating the parameters describing the characteristics of an area with modified Gaussian least square differential correction algorithm and the step size limitation filter according to the amount of updates. We have applied the algorithm for matching a sampled area with a target area supposed that the area data are available from Radar Terrain Sensor(RTS) and Reference Altitude Sensor(RAS)

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.421-424
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• 2000

In this paper, we propose a window warping method to solve stereo matching problems in projective distortion region. Because the projective distortion region cant t be estimated with fixed-size block matching algorithm, we use the window warping technique in block matching process. The position of a reference window to resample is obtained adaptively according to the degree of reliability in disparity estimated previously. The initial disparity and reliability are obtained by applying a well known hierarchical strategy. The experimental result shows that considerable improvement is obtained in the projective distortion region.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.22-33
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• 2019

This paper introduces an efficient fingerprint matching method based on multiple reference minutiae points. First, we attempt to effectively align two fingerprints by employing multiple reference minutiae points. However, the corresponding minutiae points between two fingerprints are ambiguous since a minutia of one fingerprint can be a match to any minutia of the other fingerprint. Therefore, we introduce a novel method based on linear classification concept to establish minutiae correspondences between two fingerprints. Each minutiae correspondence represents a possible alignment. For each possible alignment, a matching score is computed using minutiae and ridge orientation features and the maximum score is then selected to represent the similarity of the two fingerprints. The proposed method is evaluated using fingerprint databases, FVC2002 and FVC2004. In addition, we compare our approach with two existing methods and find that our approach outperforms them in term of matching accuracy, especially in the case of non-linear distorted fingerprints. Furthermore, the experiments show that our method provides additional advantages in low quality fingerprint images such as inaccurate position, missing minutiae, and spurious extracted minutiae.