• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.4
• /
• pp.677-685
• /
• 2008

This paper proposes algorithm for the obstacle detection and classification using a single laser scanner. In a measurement data from a laser scanner, there exist points with large differential value called singular points, which can be used to obtain the boundary of an obstacle such that obstacle information can be analyzed. On the other hand, measurement data include a lot of measurement error, which makes it difficult to analyze the accurate obstacle information. To solve this problem, the least square estimation algorithm is used to obtain the accurate information using a single laser scanner, by compensation for the measurement error. This algorithm can be used for the effective obstacle avoidance of mobile robots, and the experimental results are included to demonstrate the effectiveness of the propose algorithm.

• Journal of Auto-vehicle Safety Association
• /
• v.9 no.3
• /
• pp.24-30
• /
• 2017

This paper presents the development of laser scanner based static obstacle detection algorithm for vehicle localization on lane lost section. On urban autonomous driving, vehicle localization is based on lane information, GPS and digital map is required to ensure. However, in actual urban roads, the lane data may not come in due to traffic jams, intersections, weather conditions, faint lanes and so on. For lane lost section, lane based localization is limited or impossible. The proposed algorithm is designed to determine the lane existence by using reliability of front vision data and can be utilized on lane lost section. For the localization, the laser scanner is used to distinguish the static object through estimation and fusion process based on the speed information on radar data. Then, the laser scanner data are clustered to determine if the object is a static obstacle such as a fence, pole, curb and traffic light. The road boundary is extracted and localization is performed to determine the location of the ego vehicle by comparing with digital map by detection algorithm. It is shown that the localization using the proposed algorithm can contribute effectively to safe autonomous driving.

• Proceedings of the Korea Society of Information Technology Applications Conference
• /
• 2005.11a
• /
• pp.173-176
• /
• 2005

This paper proposes a fuzzy correction algorithm that can correct the distorted medical image caused by the scanning nonlinear velocity of the Digital X-ray Scanner System (DX-Scanner) using the Multichannel Ionization Chamber (MIC). In the DX-Scanner, the scanned medical image is distorted for reasons of unsuitable integration time at the nonlinear acceleration period of the AC servo motor during the inspection of patients. The proposed algorithm finds the nonlinear motor velocity modeling through fuzzy system by clustering and reconstructs the normal medical image lines by calculating the suitable moving distance with the velocity of the motor using the modeling, acceleration time and integration time. In addition, several image processing is included in the algorithm. This algorithm analyzes exact pixel lines by comparing the distance of the acceleration period with the distance of the uniform velocity period in every integration time and is able to compensate for the velocity of the acceleration period. By applying the proposed algorithm to the test pattern for checking the image resolution, the effectiveness of this algorithm is verified. The corrected image obtained from distorted image is similar to the normal and better image for a doctor's diagnosis.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.9 no.3
• /
• pp.232-237
• /
• 2009

Active safety vehicle devices are getting more attention recently. To prevent traffic accidents, the environment in front and even around the vehicle must be checked and monitored. In the present applications, mainly camera and radar based systems are used as sensing devices. Laser scanner, one of the sensing devices, has the advantage of obtaining accurate measurement of the distance and the geometric information about the objects in the field of view of the laser scanner. However, there is a problem that detecting object occluded by a foreground one is difficult. In this paper, criterions are proposed to manage this problem. Simulation is conducted by vehicle mounted the laser scanner and multiple-hypothesis algorithm tracks the candidate objects. We compare the running times as multi-hypothesis algorithm parameter varies.

• Proceedings of the KIEE Conference
• /
• 2007.10a
• /
• pp.385-386
• /
• 2007

This paper proposes obstacle detection and classification algorithm using a single laser scanner. The proposed algorithm searches the object singular points using a differential equation, and finds obstacle singular points shows a boundary of obstacle. And the proposed algorithm can classify object even if several obstacles overlapped. Simulation results show the feasibility of proposed algorithm using a single laser scanner, not using several laser scanners.

• Journal of Biomedical Engineering Research
• /
• v.26 no.4
• /
• pp.207-214
• /
• 2005

Breast cancer detection using electrical impedance techniques is based on numerous experimental findings that cancerous tissues have higher electrical conductivity values than normal tissues. Lately, by taking advantage of the structure of current flows underneath a planar probe of array electrodes, a mathematical formula to find lesions from a measured trans­admittance map has been derived. In order to experimentally validate its mathematical analysis and the suggested lesion estimation algorithm, we developed a 256-channel trans-admittance scanner (TAS) for probing anomalies underneath a planar array of electrodes. In this paper, we describe the design and implementation of the TAS. Its performance together with the lesion estimation algorithm was evaluated using saline phantoms. Further studies are proposed to validate the system on human subjects.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.7
• /
• pp.1260-1272
• /
• 2008

This paper proposes object detection algorithm using laser scanner and vision system for the path planning of autonomous mobile agents. As the scanner-based method can observe the obstacles in only two dimensions, it is hard to detect the shape and the number of obstacles. On the other hand, vision-based method is sensitive to the environment and has its difficulty in the accurate distance measurement. Thus, we combine these two methods based on K-means algorithm such that the obstacle avoidance and optimal path planning of autonomous mobile agents can be achieved.

• Journal of Drive and Control
• /
• v.13 no.4
• /
• pp.14-22
• /
• 2016

This paper presents a stochastic and predictive working-risk-assessment algorithm for excavators based on a one-layer laser scanner. The one-layer laser scanner is employed to detect objects and to estimate an object's dynamic behaviors such as the position, velocity, heading angle, and heading rate. To estimate the state variables, extended and linear Kalman filters are applied in consideration of laser-scanner information as the measurements. The excavator's working area is derived based on a kinematic analysis of the excavator's working parts. With the estimated dynamic behaviors and the kinematic analysis of the excavator's working parts, an object's behavior and the excavator's working area such as the maximum, actual, and predicted areas are computed for a working risk assessment. The four working-risk levels are defined using the predicted behavior and the working area, and the intersection-area-based quantitative-risk level has been computed. An actual test-data-based performance evaluation of the designed stochastic and predictive risk-assessment algorithm is conducted using a typical working scenario. The results show that the algorithm can evaluate the working-risk levels of the excavator during its operation.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.7
• /
• pp.634-640
• /
• 2015

This paper proposes a novel three-dimensional mapping algorithm in Omni-Directional Vision SLAM based on a fisheye image and laser scanner data. The performance of SLAM has been improved by various estimation methods, sensors with multiple functions, or sensor fusion. Conventional 3D SLAM approaches which mainly employed RGB-D cameras to obtain depth information are not suitable for mobile robot applications because RGB-D camera system with multiple cameras have a greater size and slow processing time for the calculation of the depth information for omni-directional images. In this paper, we used a fisheye camera installed facing downwards and a two-dimensional laser scanner separate from the camera at a constant distance. We calculated fusion points from the plane coordinates of obstacles obtained by the information of the two-dimensional laser scanner and the outline of obstacles obtained by the omni-directional image sensor that can acquire surround view at the same time. The effectiveness of the proposed method is confirmed through comparison between maps obtained using the proposed algorithm and real maps.

• Journal of Drive and Control
• /
• v.16 no.2
• /
• pp.22-29
• /
• 2019

This study presented a model predictive tracking control algorithm of autonomous truck based on object state estimation using extended Kalman filter. To design the model, the 1-layer laser scanner was used to estimate position and velocity of the object using extended Kalman filter. Based on these estimations, the desired linear path for object tracking was computed. The lateral and yaw angle errors were computed using the computed linear path and relative positions of the truck. The computed errors were used in the model predictive control algorithm to compute the optimal steering angle for object tracking. The performance evaluation was conducted on Matlab/Simulink environments using planar truck model and actual point data obtained from laser scanner. The evaluation results showed that the tracking control algorithm developed in this study can track the object reasonably based on the model predictive control algorithm based on the estimated states.