• 조명전기설비학회논문지
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• 제24권8호
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• pp.119-125
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• 2010

본 논문에서는 초음파를 이용하여 자율 운행을 하는 이동로봇이 가지는 물리적인 문제점을 해결하기 위한 방안을 제시 한다. 이동 로봇이 주변 환경을 인지함에 있어서 각종 센서를 사용한다. 그러한 센서들은 항상 올바른 값을 주지 않는다. 센서값에는 항상 노이즈가 포함되어 있는데 이것을 해결하기 위해서 학습 알고리즘인 SVR(Support Vector Regression)을 사용하여 주변 환경을 센싱한 초음파 값을 토대로 주변 환경을 추정할 수 있다. SVR을 사용하기 위해서는 SVR의 요소인 parameter와 커널을 선정해야 한다. SVR의 요소를 선정함에 있어서 정해진 값이 존재하지 않기 때문에 실험을 통해서 가장 적합한 parameter 값을 선정해야 한다. 또한 커널을 선정함에 있어서는 일반화가 가장 잘 되어 있는 RBF(Radial Basis Function)커널을 사용하였다. 본 논문에서는 세가지 환경에서의 실험을 통하여 SVR을 이용하여 센서값의 오류를 개선할 수 있음을 나타내었다.

• 제어로봇시스템학회논문지
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• 제17권2호
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• pp.93-101
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• 2011

For mobile robot, recognizing its current location is very important to navigate autonomously. Especially, loop closing detection that robot recognize location where it has visited before is a kernel problem to solve localization. A considerable amount of research has been conducted on loop closing detection and localization based on appearance because vision sensor has an advantage in terms of costs and various approaching methods to solve this problem. In case of scenes that consist of repeated structures like in corridors, perceptual aliasing in which, the two different locations are recognized as the same, occurs frequently. In this paper, we propose an improved method to recognize location in the scenes which have similar structures. We extracted salient regions from images using visual attention model and calculated weights using distinctive features in the salient region. It makes possible to emphasize unique features in the scene to classify similar-looking locations. In the results of corridor recognition experiments, proposed method showed improved recognition performance. It shows 78.2% in the accuracy of single floor corridor recognition and 71.5% for multi floor corridors recognition.

• 대한기계학회논문집A
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• 제30권9호
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• pp.1124-1133
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• 2006

This paper addresses the localization and navigation problem using invisible two dimensional barcodes on the floor. Compared with other methods using natural/artificial landmark, the proposed localization method has great advantages in cost and appearance, since the location of the robot is perfectly known using the barcode information after the mapping is finished. We also propose a navigation algorithm which uses the topological structure. For the topological information, we define nodes and edges which are suitable for indoor navigation, especially for large area having multiple rooms, many walls and many static obstacles. The proposed algorithm also has an advantage that errors occurred in each node are mutually independent and can be compensated exactly after some navigation using barcode. Simulation and experimental results. were performed to verify the algorithm in the barcode environment, and the result showed an excellent performance. After mapping, it is also possible to solve the kidnapped case and generate paths using topological information.

• 로봇학회논문지
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• 제6권2호
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• pp.156-164
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• 2011

This paper propose a localization system of indoor mobile robots. The localization system includes camera and artificial landmarks for global positioning, and encoders and gyro sensors for local positioning. The Kalman filter is applied to take into account the stochastic errors of all sensors. Also we develop a dead reckoning system to estimate the global position when the robot moves the blind spots where it cannot see artificial landmarks, The learning engine using modular networks is designed to improve the performance of the dead reckoning system. Experimental results are then presented to verify the usefulness of the proposed localization system.

• 한국측량학회지
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• 제37권2호
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• pp.45-53
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• 2019

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.

• 스마트미디어저널
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• 제12권4호
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• pp.41-46
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• 2023

UWB는 실내 측위를 위해 사용되는 기술의 일종으로 RSSI 기반의 기법들보다 정확도가 높은 특징이 있다. ROS(Robot Operating System) 기반으로 동작하는 이동체 장비는 라이다와 카메라를 사용하여 장비 주변의 환경을 모니터링할 수 있다. 이러한 모니터링 과정에서 처음 시작하는 위치를 파악하는 루프 클로징 기법 적용 시 기존의 방법은 영상 상에 특징점이 있어야 클로징 작업이 일어나기 때문에 정확도가 낮은 문제가 있다. 본 논문에서는 이러한 문제를 해결하기 위해 이동체 장비에 UWB 태그를 탑재하여 위치 정보를 제공함으로써 루프 클로징 작업의 정확도를 높이는 시스템을 설계하였다. 또한 실험을 통해서 UWB 기반 실내 측위 시스템의 정확도를 평가하였고 이를 루프 클로징 기법에 활용할 수 있는지 검증하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.306-309
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• 1996

This paper presents a method of landmark recognition in indoor environments using a neural-network for an autonomous mobile robot. In order to adapt to image deformation of a landmark resulted from variations of view-points and distances, a multi-labeled template matching(MLTM) method and a dynamic area search method(DASM) are proposed. The MLTM is. used for matching an image template with deformed real images and the DASM is proposed to detect correct feature points among incorrect feature points. Finally a feed-forward neural-network using back-propagation algorithm is adopted for recognizing the landmark.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.724-728
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• 2005

This paper represents a development of a range finder sensor module for indoor 2-D mapping and modified Hough transformation for map building. A range finder sensor module has been developed by using optic PSD (Position Sensitive Detector) sensor array at a low price. While PSD sensor is cost effective and light weighting, it has switching noise and white noise. To remove these noises, we propose a heuristic filter. For line-based map building, also we proposed advanced Hough transformation and navigation algorithm. Some experiments were illustrated for the validity of the developed system.

• 제어로봇시스템학회논문지
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• 제16권1호
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• pp.40-47
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• 2010

This paper presents a simultaneous localization and mapping (SLAM) of an indoor hallway environment using Rao-Blackwellized particle filter (RBPF) along with a line segment as a landmark. Based on the fact that fluent line features can be extracted around the ceiling and side walls of hallway using vision sensor, a horizontal line segment is extracted from an edge image using Hough transform and is also tracked continuously by an optical flow method. A successive observation of a line segment gives initial state of the line in 3D space. For data association, registered feature and observed feature are matched in image space through a degree of overlap, an orientation of line, and a distance between two lines. Experiments show that a compact environmental map can be constructed with small number of horizontal line features in real-time.

• International Journal of Control, Automation, and Systems
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• 제6권1호
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• pp.62-75
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• 2008

Vision-based simultaneous localization and map building using a single camera, while compelling in theory, have not until recently been considered extensive in the practical realm of the real world. In this paper, we propose a simple framework for the monocular SLAM of an indoor mobile robot using natural line features. Our focus in this paper is on presenting a novel approach for modeling the landmark before integration in monocular SLAM. We also discuss data association improvement in a particle filter approach by using the feature management scheme. In addition, we take constraints between features in the environment into account for reducing estimated errors and thereby improve performance. Our experimental results demonstrate the feasibility of the proposed SLAM algorithm in real-time.