• The Journal of Korea Robotics Society
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• v.3 no.2
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• pp.99-105
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• 2008

To achieve autonomous mobile robot navigation, accurate localization technique is the fundamental issue that should be addressed. In augmented reality, the position of a user is required for location-based services. This paper presents indoor localization using infrared reflective artificial landmarks. In order to minimize the disturbance to the user and to provide the ease of installation, the passive landmarks are used. The landmarks are made of coated film which reflects the infrared light efficiently. Infrared light is not visible, but the camera can capture the reflected infrared light. Once the artificial landmark is identified, the camera's relative position/orientation is estimated with respect to the landmark. In order to reduce the number of the required artificial landmarks for a given environment, the pan/tilt mechanism is developed together with the distortion correction algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.4
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• pp.375-381
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• 2011

This paper deals with the improvement of visual path following through velocity variation according to the coordinate of feature points. Visual path follow first teaches driving path by selecting milestone images then follows the route by comparing the milestone image and current image. We follow the visual path following algorithm of Chen and Birchfield [8]. In [8], they use fixed translational and rotational velocity. We propose an algorithm that uses different translational velocity according to the driving condition. Translational velocity is adjusted according to the variation of the coordinate of feature points on image. Experimental results including diverse indoor cases show the feasibility of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.387-390
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• 1993

The most important technique for an indoor robot navigation is to find out the direction and the distance from the current location to the destination through the information achieved from the sensor. For this purpose, we suggest sensor-based local homing method which compares the destination sensory data with the current location. As for the sensors, we use the CCD camera and the ultrasonic sensor, and recorded entire 360 degree panoramic data. We match the features of the image data, and the distance and the direction of the matched point will be considered as fuzzy numbers. Through a simple fuzzy arithmetic, we infer the geometric relations between the current location and the destination location.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1767-1768
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• 2007

This paper represents a map building system of Embedded Linux mobile robot. We propose a localization method which uses multiple sensors such as indoor GPS and encoder sensor for simultaneous map building system. In this paper we proposed a multiple sensor system for SLAM. For this, we developed a sensor based navigation algorithm and grid based map building algorithm under the Embedded Linux O.S. We proved this system's validity through field test

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.830-835
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• 2003

This paper describes a sonic polygonal multiple reflection range sensor (SPMRS), which uses multiple reflection properties usually ignored in ultrasonic sensors as disturbances or noises. Targets such as a plane, corner, edge, or cylinder in indoor environments can easily be detected by the multiple reflection patterns obtained with a SPMRS system. Target classification and feature data extraction, such as distance and azimuth to the target, are computed simultaneously by considering the geometrical relationships between the detected targets, and finally the environment model is generated by refining the detected targets. In addition, the narrow field of view of a sonar range sensor is increased and the scanning time is reduced by active motion of the SPMRS stepping servomechanism.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.370-371
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• 2015

자율 주행 로봇의 기본적인 기능에는 위치 추정 기능과 무선 통신 기능이 포함된다. 이미지 센서를 이용하여 로봇의 이동 위치를 추정하고, 무선통신은 ZigBee를 적용하였다. 본 논문에서는 자율 주행 로봇의 이동 위치 정보를 이미지센서를 이용하여 데이터를 취득 후 마우스 알고리즘을 통해 이동 데이터로 환산하였으며, 이동 데이터를 ZigBee통신을 통해 서버와 실시간 통신을 하였다. 이를 통해 로봇의 이동 정보를 실시간으로 취득할 수 있는 실내형 로봇 위치 추정 시스템을 구현하였다.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.220-229
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• 2018

In the midst of disaster, such as an earthquake or a nuclear radiation exposure area, there are huge risks to send human crews. Many robotic researchers have studied to send UGVs in order to replace human crews at dangerous environments. So far, two-dimensional camera information has been widely used for teleoperation of UGVs. Recently, three-dimensional information based teleoperations are attempted to compensate the limitations of camera information based teleoperation. In this paper, the 3D map information of indoor and outdoor environments reconstructed in real-time is utilized in the UGV teleoperation. Further, we apply the LTE communication technology to endure the stability of the teleoperation even under the deteriorate environment. The proposed teleoperation system is performed at explosive disposal missions and their feasibilities could be verified through completion of that missions using the UGV with the Explosive Ordnance Disposal (EOD) team of Busan Port Security Corporation.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.237-247
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• 2018

In this paper, we describe a sensor module that monitors fire environment by analyzing fire characteristics. We analyzed the smoke characteristics of indoor fire. Six different environments were defined according to the type of smoke and the flame, and the sensors available for each environment were combined. Based on this analysis, the sensors were selected from the perspective of firefighter. The sensor module consists of an RGB camera, an infrared camera and a radar. It is designed with minimum weight to fit on the robot. the enclosure of sensor is designed to protect against the radiant heat of the fire scene. We propose a single camera mode, thermal stereo mode, data fusion mode, and radar mode that can be used depending on the fire scene. Thermal stereo was effectively refined using an image segmentation algorithm, SLIC (Simple Linear Iterative Clustering). In order to reproduce the fire scene, three fire test environments were built and each sensor was verified.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.122-129
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• 2021

For the Simultaneous Localization and Mapping (SLAM) problem, a different path results in different SLAM results. Usually, SLAM follows a trail of input data. Active SLAM, which determines where to sense for the next step, can suggest a better path for a better SLAM result during the data acquisition step. In this paper, we will use reinforcement learning to find where to perceive. By assigning entire target area coverage to a goal and uncertainty as a negative reward, the reinforcement learning network finds an optimal path to minimize trajectory uncertainty and maximize map coverage. However, most active SLAM researches are performed in indoor or aerial environments where robots can move in every direction. In the urban environment, vehicles only can move following road structure and traffic rules. Graph structure can efficiently express road environment, considering crossroads and streets as nodes and edges, respectively. In this paper, we propose a novel method to find optimal SLAM path using graph structure and reinforcement learning technique.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.1-10
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• 2022

Single-view reconstruction (SVR) is a fundamental method in computer vision. Often used for reconstructing human-made environments, the Manhattan world assumption presumes that planes in the real world exist in mutually orthogonal directions. Accordingly, this paper addresses an automatic SVR algorithm for Manhattan worlds. A method for estimating the directions of planes using graph-cut optimization is proposed. After segmenting an image from extracted line segments, the data cost function and smoothness cost function for graph-cut optimization are defined by considering the directions of the line segments and neighborhood segments. Furthermore, segments with the same depths are grouped during a depth-estimation step using a minimum spanning tree algorithm with the proposed weights. Experimental results demonstrate that, unlike previous methods, the proposed method can identify complex Manhattan structures of indoor and outdoor scenes and provide the exact boundaries and intersections of planes.