• 로봇학회논문지
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• 제4권4호
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• pp.312-319
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• 2009

This paper proposes a low-complexity indoor localization method of mobile robot under the dynamic environment by fusing the landmark image information from an ordinary camera and the distance information from sensor nodes in an indoor environment, which is based on sensor network. Basically, the sensor network provides an effective method for the mobile robot to adapt to environmental changes and guides it across a geographical network area. To enhance the performance of localization, we used an ordinary CCD camera and the artificial landmarks, which are devised for self-localization. Experimental results show that the real-time localization of mobile robot can be achieved with robustness and accurateness using the proposed localization method.

• 로봇학회논문지
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• 제9권4호
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• pp.232-241
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• 2014

This paper proposes an underwater localization algorithm using probabilistic object recognition. It is organized as follows; 1) recognizing artificial objects using imaging sonar, and 2) localizing the recognized objects and the vehicle using EKF(Extended Kalman Filter) based SLAM. For this purpose, we develop artificial landmarks to be recognized even under the unstable sonar images induced by noise. Moreover, a probabilistic recognition framework is proposed. In this way, the distance and bearing of the recognized artificial landmarks are acquired to perform the localization of the underwater vehicle. Using the recognized objects, EKF-based SLAM is carried out and results in a path of the underwater vehicle and the location of landmarks. The proposed localization algorithm is verified by experiments in a basin.

• 한국컴퓨터정보학회논문지
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• 제10권6호
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• pp.177-186
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• 2005

본 논문에서는 DEM(Digital Elevation Model)과 산 영상을 매핑하여 3차원 정보를 생성하고 이를 이용한 비전기반 카메라 위치인식방법을 제안한다. 일반적으로 인식에 사용된 영상의 특징들은 카메라뷰에 따라 내용이 변해 정보양이 증가하는 단점이 있다. 본 논문에서는 카메라뷰에 무관한 기하학의 불변특징을 추출하고 제안하는 유사도 평가함수와 Graham 탐색방법을 사용한 정확한 대응점을 산출하여 카메라 외부인수를 계산하였다. 또한 그래픽이론과 시각적 단서를 이용한 3차원 정보생성 방법을 제안하였다. 제안하는 방법은 불변 점 특징 추출단계, 3차원 정보 생성단계, 외부인수 산출단계의 3단계로 구성된다. 실험에서는 제안한 방법과 기존방법을 비교, 분석함으로써 제안한 방법의 우월성을 입증하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권2호
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• pp.161-166
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• 2006

Localization of mobile agent within a sensing network is a fundamental requirement for many applications, using networked navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems, This paper describes a networked sensor-based navigation method in an indoor environment for an autonomous mobile robot which can navigate and avoid obstacle. In this method, the self-localization of the robot is done with a model-based vision system using networked sensors, and nonstop navigation is realized by a Kalman filter-based STSF(Space and Time Sensor Fusion) method. Stationary obstacles and moving obstacles are avoided with networked sensor data such as CCD camera and sonar ring. We will report on experiments in a hallway using the Pioneer-DX robot. In addition to that, the localization has inevitable uncertainties in the features and in the robot position estimation. Kalman filter scheme is used for the estimation of the mobile robot localization. And Extensive experiments with a robot and a sensor network confirm the validity of the approach.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.1010-1013
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• 2003

This paper describes an algorithm for determining robot location using appearance-based paradigm. This algorithm compress the image set using PCA(principal component analysis) to obtain a low-dimensional subspace, called the eigenspace, and it makes a manifold that represent a continuous-appearance function. To determine robot location, given an unknown input image, the recognition system first projects the image to eigenspace. Neural network use coefficients of the eigenspace to estimate the location of the mobile robot. The algorithm has been implemented and tested on a mobile robot system. In several trials it computes location accurately.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.240-251
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• 2016

This paper presents a real-time indoor location and pose estimation method that utilizes simple artificial markers and image analysis techniques for the purpose of warehouse automation. The conventional indoor localization methods cannot work robustly in warehouses where severe environmental changes usually occur due to the movement of stocked goods. To overcome this problem, the proposed framework places artificial markers having different interior pattern on the predefined position of the warehouse floor. The proposed algorithm obtains marker candidate regions from a captured image by a simple binarization and labeling procedure. Then it extracts maker interior pattern information from each candidate region in order to decide whether the candidate region is a true marker or not. The extracted interior pattern information and the outer boundary of the marker are used to estimate location and heading angle of the localization system. Experimental results show that the proposed localization method can provide high performance which is almost equivalent to that of the conventional method using an expensive LIDAR sensor and AMCL algorithm.

• 전기학회논문지
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• 제63권1호
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• pp.99-107
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• 2014

In this paper, a landmark based localization system using a Kinect sensor is proposed and evaluated with the implemented system for precise and autonomous navigation of low cost robots. The proposed localization method finds the positions of landmark on the image plane and the depth value using color and depth images. The coordinates transforms are defined using the depth value. Using coordinate transformation, the position in the image plane is transformed to the position in the body frame. The ranges between the landmarks and the Kinect sensor are the norm of the landmark positions in body frame. The Kinect sensor position is computed using the tri-lateral whose inputs are the ranges and the known landmark positions. In addition, a new matching method using the pin hole model is proposed to reduce the mismatch between depth and color images. Furthermore, a height error compensation method using the relationship between the body frame and real world coordinates is proposed to reduce the effect of wrong leveling. The error analysis are also given to find out the effect of focal length, principal point and depth value to the range. The experiments using 2D bar code with the implemented system show that the position with less than 3cm error is obtained in enclosed space($3,500mm{\times}3,000mm{\times}2,500mm$).

• 제어로봇시스템학회논문지
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• 제14권8호
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• pp.767-773
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• 2008

This paper presents a localization system based on the use of electric compass and tracking IR light source. Digital RGB(Red, Green, Blue)signal of digital CMOS Camera is sent to CPLD which converts the color image to binary image at 30 frames per second. CMOS camera has IR filter and UV filter in front of CMOS cell. The filters cut off above 720nm light source. Binary output data of CPLD is sent to DSP that rapidly tracks the IR light source by moving Camera tilt DC motor. At a robot toward north, electric compass signals and IR light source angles which are used for calculating the data of the location system. Because geomagnetic field is linear in local position, this location system is possible. Finally, it is shown that position error is within ${\pm}1.3cm$ in this system.

• 한국ITS학회 논문지
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• 제20권5호
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• pp.186-201
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• 2021

자율 주행 기술이 발전함에 따라 주행 주변 환경을 인식하는 데 차량 위치의 정확성은 매우 중요하다. 측위의 정확도를 높이기 위해 정밀지도를 사용한 지도 정합 측위기술(map-matching localization)이 연구되고 있다. 기존의 지도 정합 기법은 지도에서 차선의 중심으로 표현된 데이터를 기반으로 차량 위치를 추정하기에 차선 내 측면 거리의 편차를 반영하지 않는다. 따라서 본 논문에서는 정밀한 측위를 제공하기 위해 영상처리를 통한 차선 검출 기법과 정밀지도의 차선 위치 정보를 이용한 기법을 제안한다. 영상 처리 기법으로 IPM(inverse perspective mapping)과 다중 차선 검출 기법, 중앙선 검출 기법을 통하여 차선 번호를 검출하고 차선 이탈 감지 방법으로 차선 중심으로부터 차량의 측면 거리를 추정한다. 최종적으로 영상처리로 검출한 차선 번호와 GNSS / INS의 위치를 기반으로 정밀지도에서 위치 링크정보를 추출하고 추출된 링크에 측면 거리를 반영하여 차선 내 차량의 위치를 추정한다. 제안된 방법의 성능을 평가하기 위하여 실제 도로에서 실험하였다. 제안하는 방법은 GNSS / INS와 비교 시 약 1.0m 정도 정확도가 개선되며, 기존의 차선레벨 맵매칭 방법과 비교 시 구간별로 약 0.04m ~ 0.21m(7~30%) 정확도가 개선됨을 확인하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.1173-1174
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• 2008

In this paper, we propose a front-mounted single camera based depth estimation and robot localization method. The advantage of front-mounted camera is reduction of redundancy when the robot move. The robot computes depth information of captured image, moving around. And the robot location is corrected by depth information.