• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.11
• /
• pp.937-942
• /
• 2003

We present a self-localization method for mobile robots using vertical line features of indoor environment. When a 2D map including feature points and color information is given, a mobile robot moves to the destination, and acquires images from the surroundings having vertical line edges by one camera. From the image, vertical line edges are detected, and pattern vectors meaning averaged color values of the left and right regions of the each line are computed by using the properties of the line and a region growing method. The pattern vectors are matched with the feature points of the map by comparing the color information and the geometrical relationship. From the perspective transformation and rigid transformation of the corresponded points, nonlinear equations are derived. Localization is carried out from solving the equations by using Newton's method. Experimental results show that the proposed method using mono view is simple and applicable to indoor environment.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1931-1935
• /
• 2004

A location estimate problem is critical issues for mobile robot. Because it is basic problem in practical use of the mobile robot which do what, or move where, or reach an aim. Already there are many technologies of robot localization (like GPS, vision, sonar sensor, etc) used on development. But the elevation of accurateness was brought the problem that must consider an increase of a hardware cost and addition electric power in each ways. There is the core in question to develop available and accurate sensing algorithm though it is economical. We used a ultrasonic sensor and was going to implement comparatively accurate localization though economical. Using a sensing data, we could make a grid map and estimate a position of a mobile robot. In this paper, to get a satisfactory answer about this problem using a ultrasonic sensor.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.793-797
• /
• 2003

In this paper, we present a self-localization method for mobile robots using vertical line features of indoor environment. When a 2D map including feature points and color information is given, a mobile robot moves to the destination, and acquires images by one camera from the surroundings having vertical line edges. From the image, vertical line edges are detected, and pattern vectors meaning averaged color values of the left and right region of each line segment are computed. The pattern vectors are matched with the feature points of the map using the color information and the geometrical relationship of the points. From the perspective transformation of the corresponded points, nonlinear equations are derived. Localization is carried out from solving the equations by using Newton's method. Experimental results show that the proposed method using mono view is simple and applicable to indoor environment.

• Journal of Agricultural Extension & Community Development
• /
• v.7 no.1
• /
• pp.247-254
• /
• 2000

Since 1997, Korean government changed the status of extension educators from central government staff to county/city government staff for the sake of democratization and localization, regardless of opposition of academic society of extension professionals. Localization of extension educators followed by government restructuring in Korea, resulted various problems, such as 1) Decreased morale and number of extension educators, thus weakening the extension educational function, 2) Weakened linkages between national and local extension offices, 3) Less opportunity for in-service edition of extension educators, 4) Weakened linkages between agricultural research and extension, etc. During the last 30 years, extension services have helped Korean farmers in achieving self sufficiency of rice and year around supply of green vegetables by diffusion of vinyl house technology. Various problems after three years of localization of extension educators were discussed between announcer of radio KBS and a professor of extension in an interview format, and it was concluded that national efforts should be needed to restore morale of extension educators to continually achieve the national goals of food production, balanced national development and preservation of environment, at the same time to meet increasing needs of farmers.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.12
• /
• pp.6420-6426
• /
• 2013

The indoor areas for the commercial use of automatic monitoring systems of mobile robot localization improves the cognitive abilities and the needs of the environment with this emerging and existing mobile robot localization, and object recognition methods commonly around its great sensor are leveraged. On the other hand, there is a difficulty with a problem-solving self-location estimation in indoor mobile robots using only the sensors of the robot. Therefore, in this paper, a self-position estimation method for an enhanced and effective mobile robot is proposed using a marker and CCTV video that is already installed in the building. In particular, after recognizing a square mobile robot and the object from the input image, and the vertices were confirmed, the feature points of the marker were found, and marker recognition was then performed. First, a self-position estimation of the mobile robot was performed according to the relationship of the image marker and a coordinate transformation was performed. In particular, the estimation was converted to an absolute coordinate value based on CCTV information, such as robots and obstacles. The study results can be used to make a convenient self-position estimation of the robot in the indoor areas to verify the self-position estimation method of the mobile robot. In addition, experimental operation was performed based on the actual robot system.

• The KIPS Transactions:PartB
• /
• v.15B no.2
• /
• pp.137-146
• /
• 2008

Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.155.1-155
• /
• 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 Institute of Intelligent Systems Conference
• /
• 2007.04a
• /
• pp.15-18
• /
• 2007

이 논문에서는 Encoder, Gyro, 다수의 IR센서를 이용한 실내용 이동로봇의 자기위치 추정에 관한 방법 중 첫 번째 실험으로 다수의 IR센서로부터 획득한 거리데이터를 이용하여 작성한 환경지도에서 모서리를 검출하고, 이를 바탕으로 각 센서에서 측정된 데이터를 병합하도록 하였다. 마지막으로 얻어진 환경지도와 실제 환경을 비교하여 그 성능을 평가하였다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.5
• /
• pp.560-566
• /
• 2005

In this paper, a practical technique for correction of a distorted image for vision-based localization of ubiquitous mobile robot. The localization of mobile robot is essential and is realized by using camera vision system. In order to wide the view angle of camera, the vision system includes a fish-eye lens, which distorts the image. Because a mobile robot moves rapidly, the image processing should he fast to recognize the localization. Thus, we propose the practical correction technique for a distorted image, verify the Performance by experimental test.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.6
• /
• pp.2044-2059
• /
• 2022

Recent works have shown that the geometric constraint can be harnessed to boost the performance of CNN-based camera localization. However, the existing strategies are limited to imposing image-level constraint between pose pairs, which is weak and coarse-gained. In this paper, we introduce a pixel-level epipolar geometry constraint to vanilla localization framework without the ground-truth 3D information. Dubbed EpiLoc, our method establishes the geometric relationship between pixels in different images by utilizing the epipolar geometry thus forcing the network to regress more accurate poses. We also propose a variant called EpiSingle to cope with non-sequential training images, which can construct the epipolar geometry constraint based on a single image in a self-supervised manner. Extensive experiments on the public indoor 7Scenes and outdoor RobotCar datasets show that the proposed pixel-level constraint is valuable, and helps our EpiLoc achieve state-of-the-art results in the end-to-end camera localization task.