• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1416-1418
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• 2004

This paper proposes an image processing algorithm for detecting obstacles on road-lane using DLI(disparity of lane-related information) that is generated by stereo images acquired from dual cameras mounted on a moving vehicle. The DLI is a disparity that is acquired using single lane information from road lane detection. For the purpose to reduce processing time, we use small blocks obtained by edge-histogram based blocking logic. This algorithm detects moving objects such as preceding vehicles and obstacles. The proposed algorithm has been implemented in a personal computer with the road image data of a typical highway. We successfully performed experiments under a wide variety of road conditions without changing parameter values or adding human intervention. Experimental results also showed that the proposed DLI is quite successful.

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

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.509-516
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• 2004

This paper proposes an image processing algorithm for detecting obstacles on road using DLI(disparity of lane-related information) that is generated by stereo images acquired from dual cameras mounted on a moving vehicle. The DLI is a disparity that is acquired using a single lane information from road lane detection. For the purpose to reduce processing time, we use small block of edge-histogram based blocking logic. This algorithm detects moving objects such as preceding vehicles and obstacles. The proposed algorithm has been implemented in a personal computer with the road image data of a typical highway. We successfully performed experiments under a wide variety of road conditions without changing parameter values or adding human intervention. Experimental results also showed that the proposed DLI is quite successful.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.522-525
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• 2015

In this paper, we propose a walking aid system that guides route to visually impaired people in order to recognize uncertain obstacles based on tactile stimulation. The proposed system is composed of the touch-based obstacle detection module, the obstacle height detection module, and the route guidance algorithms. The touch-based obstacle detection module detects each obstacle, which is located at left, right, and front of a visually impaired person by stimulating his thumb with the rotational force of the servomotor. The obstacle height detection module integrates detected data by the linear arrangement of ultrasonic sensors to identify the height of an obstacle about 3 of-phase(i.e., high, medium, low). The proposed route guidance algorithm guides an optimized path to the visually impaired person by updating his current position information based on the signal of the built-in GPS receiver in smartphone. In addition, the route guidance algorithm delivers information with speech to a visually impaired person through Bluetooth commuination in the developed route guidance app. The proposed system can create a path to avoid the obstacles by recognizing the placed situation of the obstacles with exploring the uncertain path.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.552-557
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• 2009

In this paper, we propose hybrid navigation system, for obstacle detection and avoidance in Intelligent wheelchairs (IWs). To robustly detect obstacles and avoid them on various environments, hybrid navigation system combines both range-sensor and camera information. For this, 10 range-sensors (2 ultrasonic and 8 infra-red sensors) and CCD camera are used. Through processing the informations obtained from those sensors, our system can detect obstacles with various sizes and shapes, and then avoid them. To assess the effectiveness of the proposed hybrid navigation system, it was tested on complex environments including various obstacles, then the results showed the potential of our system as mobility aids for disabled people.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.1
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• pp.93-103
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• 1999

To detect obstacles and lane-markers for driving vehicles, a new moving window scheme where moving windows are assigned to an image frame captured by a camera is addressed. For the detection of obstacles, it is important to estimate lane-markers precisely and rapidly. For this purpose, selecting some partes of an image frame at the expected lane locations, i.e., selecting window are generally adopted for extracting lane-markers efficiently. In this paper, a new scheme that extracts lane-markers precisely by assigning variable size windows at the expected locations of lane-markers considering the road curvature and finally detects obstacles within a driving lane is proposed. The accuracy improvement using this moving window scheme is showed by comparing to the conventional fixed window method and to using radar to laser sensors.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.487-495
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• 2023

This paper presents a method for autonomous exploration of indoor environments using a 2-dimensional Light Detection And Ranging (LiDAR) scanner. The proposed frontier-based exploration method considers navigability from the current robot position to extracted frontier targets. An approach to constructing the point cloud grid map that accurately reflects the occupancy probability of glass obstacles is proposed, enabling identification of safe frontier grids on the safety grid map calculated from the point cloud grid map. Navigability, indicating whether the robot can successfully navigate to each frontier target, is calculated by applying the skeletonization-informed rapidly exploring random tree algorithm to the safety grid map. While conventional exploration approaches have focused on frontier detection and target position/direction decision, the proposed method discusses a safe navigation approach for the overall exploration process until the completion of mapping. Real-world experiments have been conducted to verify that the proposed method leads the robot to avoid glass obstacles and safely navigate the entire environment, constructing the point cloud map and calculating the navigability with low computing time deviation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1444-1450
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• 2011

An analysis of the causes of railroad crossing accidents reveals that most train collision accidents that occur when safety crossing devices are functioning normally occur because vehicles either experience engine failure on the tracks or because drivers were not notified of the coming train, in which case they get trapped on the tracks when the crossing barriers descend. To prevent such an accident, obstacle detection device by using laser beams detecting the presence of obstacle and crossing bar direction controller by moving direction detection sensor using the Earth's magnetic field detection technology are used in the railroad crossing. Despite using the obstacles detector and crossing bar direction controller in the railroad crossing, the equipments for the railroad crossing does not prevent accidents completely. Therefore, this research has studied new method that can detect obstacles through image analyze and alternate existing equipments. There will be excellent effect to be preventing railroad crossing accident by developing a reliable and new obstacle detecting device.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.633-636
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• 1999

We present a robust scheme of detecting obstacles such as vehicles, human beings, and other artificial structures that may cause serious traffic accidents in the nighttime driving. Obstacle regions are detected by the evidential reasoning rules that combine the isolated regions obtained by the phase-directed edge-linking and the hot evidence information. Preliminary experimental results show that the performance is robust to nighttime infrared scenes having various types of obstacles

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.255-264
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• 2011

In this paper, we propose a forward vehicle detection algorithm using column detection and bird's-eye view mapping based on stereo vision. The algorithm can detect forward vehicles robustly in real complex traffic situations. The algorithm consists of the three steps, namely road feature-based column detection, bird's-eye view mapping-based obstacle segmentation, obstacle area remerging and vehicle verification. First, we extract a road feature using maximum frequent values in v-disparity map. And we perform a column detection using the road feature as a new criterion. The road feature is more appropriate criterion than the median value because it is not affected by a road traffic situation, for example the changing of obstacle size or the number of obstacles. But there are still multiple obstacles in the obstacle areas. Thus, we perform a bird's-eye view mapping-based obstacle segmentation to divide obstacle accurately. We can segment obstacle easily because a bird's-eye view mapping can represent the position of obstacle on planar plane using depth map and camera information. Additionally, we perform obstacle area remerging processing because a segmented obstacle area may be same obstacle. Finally, we verify the obstacles whether those are vehicles or not using a depth map and gray image. We conduct experiments to prove the vehicle detection performance by applying our algorithm to real complex traffic situations.