• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.277-282
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• 2011

This paper describes a sensor fusion-based semantic map building which can improve the capabilities of a mobile robot in various domains including localization, path-planning and mapping. To build a semantic map, various environmental information, such as doors and cliff areas, should be extracted autonomously. Therefore, we propose a method to detect doors, cliff areas and robust visual features using a laser scanner and a vision sensor. The GHT (General Hough Transform) based recognition of door handles and the geometrical features of a door are used to detect doors. To detect the cliff area and robust visual features, the tilting laser scanner and SIFT features are used, respectively. The proposed method was verified by various experiments and showed that the robot could build a semantic map autonomously in various indoor environments.

• Journal of the Ergonomics Society of Korea
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• v.7 no.2
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• pp.23-30
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• 1988

The methods suggested up to now for 3-dimensional anthroponetry require much efforts and costs in measuring and analysing. To solve these problems, we adopt the methods such as Moire Interferometry, Image processing and Computer Vision Technique which are efficient in processing anthropomorphic data. Moire contourgraph was constructed by using Ar-ion laser as a light source (2 Watt power and 5145 A wavelength) and laser beam expander(20X). Image data can be 3-dimensionally reconstructed as the surface patch and geographical relation between faces expressed by mash-point and edges as units. This research is focused on the followings; 1) Development of an economical and reliable measuring method. 2) Design of reproduction methods of 3-dimensional human body data. Therefore, our research makes it possible to study further advanced quantitative analysis of human body.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.4
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• pp.88-98
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• 1994

A new local path planning algorithm for free-ranging robots is proposed. Considering that a laser range finder has the excellent resolution with respect to angular and distance measurements, a simple local path planning algorithm is achieved by a directional weighting method for obtaining a heading direction of nobile robot. The directional weighting method decides the heading direction of the mobile robot by estimating the attractive resultant force which is obtained by directional weighting function times range data, and testing whether the collision-free path and the copen parthway conditions are satisfied. Also, the effectiveness of the established local path planning algorithm is estimated by computer simulation in complex environment.

• Journal of the Optical Society of Korea
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• v.18 no.4
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• pp.317-329
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• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08b
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• pp.1504-1507
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• 2007

Depth data for is very important data for 3D display. Disparity and depth data makes users to feel 3D effect. We used stereo camera to measure depth and made fast algorithm to get in real time. This vision system can be substituted for expensive laser system.

• Proceedings of the KWS Conference
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• 2000.10a
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• pp.40-42
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• 2000

This paper presents a new method of segmenting a one-dimensional signal into a set of features of type(line, Vee-groove, Lap-joint and etc.), A set of requirements for the segmentation process result from the application area, which in this case are laser welding, GMAW(Gas Metal Arc Welding), SAW(Submerged Arc Welding) and high speed tack welding. The algorithm is able to detect an exact welding position in the presence of noise and missing data, yet is reasonably economical to implement

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.593-596
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• 1996

A seam tracking control system with a tool position control and a camera orientation control, has been developed here. For the camera orientation contro, SOFNN was used to learn the expert control signal. The SOFNN algorithm can adjust the fuzzy set parameters and determine the fuzzy logic structure.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.134.5-134
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• 2001

The capability of environment recognition is very important for mobile robot. Especially, a function of target tracking is necessary in monitoring and watching an object using mobile robot. In general, vision sensors such as CCD camera and laser range finder were used for tracking of a moving target. However, they are not only affected by intensity of illumination in environment but also require high performance processors to process large amount of data. Therefore, in this paper, we propose the construction of target tracking system for mobile robot using only ultrasonic sensors to cope with these problems.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2058-2060
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• 2003

본 논문에서는 이동로봇의 실시간 영상처 리와 장애물 검출을 위한 알고리즘를 제시하였다. 단일 비젼 시스템을 사용하여 복도의 경계선을 추출하기 위하여 개선된 허프 트랜스폼 알고리즘을 적용하고 많은 연산량을 수행하기 위한 방법으로 레이져 포인터를 이용한 장애물 검출을 한다. 레이져 포인터의 레이져 빔이 장애물에 반사되어지는 정도를 영상처리를 통해 처리한 후 장애물의 유무를 판단하게 된다. 실험을 통하여 제시한 알고리즘의 우수성을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.871-875
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• 2003

To detect and avoid obstacles is one of the important tasks of mobile navigation. In a real environment, when a mobile robot encounters dynamic obstacles, it is required to simultaneously detect and avoid obstacles for its body safely. In previous vision system, mobile robot has used it as either a passive sensor or an active sensor. This paper proposes a new obstacle detection algorithm that uses a stereo camera as both a passive sensor and an active sensor. Our system estimates the distances from obstacles by both passive-correspondence and active-correspondence using laser slit. The system operates in three steps. First, a far-off obstacle is detected by the disparity from stereo correspondence. Next, a close obstacle is acquired from laser slit beam projected in the same stereo image. Finally, we implement obstacle avoidance algorithm, adopting the modified Dynamic Window Approach (DWA), by using the acquired the obstacle's distance.