• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.13 no.1
• /
• pp.58-63
• /
• 2020

This paper proposes the method for improving the localization accuracy of the mobile robot based on the stereo camera. To restore the position information from stereo images obtained by the stereo camera, the corresponding point which corresponds to one pixel on the left image should be found on the right image. For this, there is the general method to search for corresponding point by calculating the similarity of pixel with pixels on the epipolar line. However, there are some disadvantages because all pixels on the epipolar line should be calculated and the similarity is calculated by only pixel value like RGB color space. To make up for this weak point, this paper implements the method to search for the corresponding point simply by calculating the gap of x-coordinate when the feature points, which are extracted by feature extraction and matched by feature matching method, are a pair and located on the same y-coordinate on the left/right image. In addition, the proposed method tries to preserve the number of feature points as much as possible by finding the corresponding points through the conventional algorithm in case of unmatched features. Because the number of the feature points has effect on the accuracy of the localization. The position of the mobile robot is compensated based on 3-D coordinates of the features which are restored by the feature points and corresponding points. As experimental results, by the proposed method, the number of the feature points are increased for compensating the position and the position of the mobile robot can be compensated more than only feature extraction.

• The Journal of the Acoustical Society of Korea
• /
• v.12 no.1E
• /
• pp.71-77
• /
• 1993

패턴매칭 기법에 근거한 음성 인식 시스템은 크게 clustering 과정과 labeling 과정으로 구성된다. 본 논문에서는 Kohonen의 featrue map 알고리즘과 LVQ2 알고리즘을 각각 clusterer와 labeler로 하는 음소인식 시스템을 구성한다. 구성된 인식시스템의 성능을 향상시키기 위해서 수정된 LVQ2알고리즘(MLVQ2)을 제안한다. MLVQ2는 selective learning, LVQ2, perturbed LVQ2 그리고 기존의 LVQ2의 4단계 학습과정으로 구성된다. 제안된 음소 인식 알고리즘의 성능을 평가하기 위하여 LVQ2와 MLVQ2를 각각 사용하여 6가지의 한국어 음소군에 대한 feature map을 만든다. 음소인식 실험결과, LVQ2와 MLVQ2를 사용하는 경우 각각 60.5%와 65.4%의 인식률을 얻을 수 있었다.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2006.04a
• /
• pp.235-241
• /
• 2006

The demand of 3D terrain mapping techniques is increasing in many application fields such as CNS(Car Navigation System), web service system, DMB(Digital Multimedia Broadcasting) systems and etc To construct a 3D terrain map, it is a pre-requite step that register data collected from different surveying sources. This Paper Present the methodology to register airborne photo, LIDAR data, and digital map, which are major data sources to create a 3D terrain mao. For this purpose, we developed the generally applicable algorithm that uses linear features to register airborne photos and digital maps to LIDAR data. The algorithm explicitly formulates step-by-step methodologies to establish observation equations for transformation. The results clearly demonstrate the proposed algorithm is appropriate to register these data sources.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.5
• /
• pp.577-583
• /
• 2007

Simultaneous Localization and Mapping (SLAM) is the process of building a map of an unknown environment and simultaneously localizing a robot relative to this map. SLAM is very important for the indoor navigation of a mobile robot and much research has been conducted on this subject. Although feature-based SLAM using an Extended Kalman Filter (EKF) is widely used, it has shortcomings in that the computational complexity grows in proportion to the square of the number of features. This prohibits EKF-SLAM from operating in real time and makes it unfeasible in large environments where many features exist. This paper presents an algorithm which reduces the computational complexity of EKF-SLAM by using topological information (TI) extracted through a thinning process. The global map can be divided into local areas using the nodes of a thinning-based topological map. SLAM is then performed in local instead of global areas. Experimental results for various environments show that the performance and efficiency of the proposed EKF-SLAM/TI scheme are excellent.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.101.1-101
• /
• 2001

This paper describes a grid mapping for a vision based mobile robot in uncertain indoor environment. The map building is a prerequisite for navigation of a mobile robot and the problem of feature correspondence across two images is well known to be of crucial Importance for vision-based mapping We use a stereo matching algorithm obtained by singular value decomposition of an appropriate correspondence strength matrix. This new correspondence strength means a correlation weight for some local measurements to quantify similarity between features. The visual range data from the reconstructed disparity image form an occupancy grid representation. The occupancy map is a grid-based map in which each cell has some value indicating the probability at that location ...

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.572-577
• /
• 2009

This paper proposes a novel reversible image authentication method that requires neither location map nor memorization of parameters. The proposed method detects image tampering and further localizes tampered regions. Though this method once distorts an image to hide data for tamper detection, it recovers the original image from the distorted image unless no tamper is applied to the image. The method extracts hidden data and recovers the original image without memorization of any location map that indicates hiding places and of any parameter used in the algorithm. This feature makes the proposed method practical. Simulation results show the effectiveness of the proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.17 no.3
• /
• pp.197-203
• /
• 1999

Depending on geographical features and error sources in the survey field, inaccurate data is inevitable in GPS kinematic survey for positioning with feature codes. In this study, the trimmed mean and the first order differential equation are used to develop an inaccurate positioning data detection algorithm, and a cubic spline curve and a linear polynomial are used to interpolate the inaccurate data. Based on interpolated data, a digital map for 30 km range of rural highway is produced and a corresponding GIS coverage map is obtained by analyzing and solving the problem associated with the map.

• Proceedings of the IEEK Conference
• /
• 1998.06a
• /
• pp.705-708
• /
• 1998

In this paper, a new algorithm is proposed that the template matching is used to devide SOFM (self-organizig feature map) for fast learning and to extract features for considering input data types. In order to verify the superoprity of the proposed algorithm, applied to the recognition of handwritten numerals. Templates of handwritten numerals are created by a line of external-contact.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.2092-2095
• /
• 2003

In this paper, we propose an algorithm to extract rectangular object area such 3s Data Matrix two-dimensional barcode using edge tracing-based linear feature detection. Hough transform is usually employed to detect lines of edge map. However, it requires parametric image space, and does not find the location of end points of the detected lines. Our algorithm detects end points of the detected lines using edge tracing and extracts object area using its shape information.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.404-404
• /
• 2000

This paper presents a single vision-based sel(-localization method in an corridor environment. We use the Hough transform for finding parallel lines and vertical lines. And we use these cross points as feature points and it is calculated relative distance from mobile robot to these points. For matching environment map to feature points, searching window is defined and self-localization is performed by matching procedure. The result shows the suitability of this method by experiment.