• 반도체디스플레이기술학회지
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• 제21권1호
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• pp.136-141
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• 2022

In this paper, we designed, implemented, and verified the SLAM system that can be used on mobile devices. Mobile SLAM is composed of a stand-alone type that directly performs SLAM operation on a mobile device, and a mapping server type that additionally configures a mapping server based on FastAPI to perform SLAM operation on the server and transmits data for map visualization to a mobile device. The mobile SLAM system proposed in this paper is to mix the two types in order to make SLAM operation and map generation more efficient. The stand-alone type SLAM system was configured as an Android app by porting the OpenVSLAM library to the Unity engine, and the map generation and performance were evaluated on desktop PCs and mobile devices. The mobile SLAM system in this paper is an open source project, so it is expected to help develop AR contents based on SLAM in a mobile environment.

• 반도체디스플레이기술학회지
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• 제20권4호
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• pp.96-101
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• 2021

The SLAM market is growing rapidly with advances in Machine Learning, Drones, Augmented Reality technologies. However, due to the absence of an open source-based SLAM library for developing AR content, most SLAM researchers are required to conduct their own research and development to customize SLAM. In this paper, we propose an opensource-based Mobile Markerless AR System by building our own pipeline based on Visual SLAM. To implement the Mobile AR System of this paper, it uses ORB-SLAM3 and Unity Engine and We experimented with running our system in a real environment and confirming it in the Unity Engine's Mobile Viewer. Through this experimentation, we can verify that the Unity Engine and the SLAM System are tightly integrated and communicate smoothly. Also, we expect to accelerate the growth of SLAM technology through this research.

• 로봇학회논문지
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• 제15권3호
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• pp.205-211
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• 2020

In this paper, we propose a modified ORB-SLAM (Oriented FAST and Rotated BRIEF Simultaneous Localization And Mapping) for precise indoor navigation of a mobile robot. The exact posture and position estimation by the ORB-SLAM is not possible all the times for the indoor navigation of a mobile robot when there are not enough features in the environment. To overcome this shortcoming, additional IMU (Inertial Measurement Unit) and encoder sensors were installed and utilized to calibrate the ORB-SLAM. By fusing the global information acquired by the SLAM and the dynamic local location information of the IMU and the encoder sensors, the mobile robot can be obtained the precise navigation information in the indoor environment with few feature points. The superiority of the modified ORB-SLAM was verified to compared with the conventional algorithm by the real experiments of a mobile robot navigation in a corridor environment.

• International Journal of Control, Automation, and Systems
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• 제5권5호
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• pp.577-583
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• 2007

Simultaneous Localization and Mapping (SLAM) is the process of building a map of an unknown environment and simultaneously localizing a robot relative to this map. SLAM is very important for the indoor navigation of a mobile robot and much research has been conducted on this subject. Although feature-based SLAM using an Extended Kalman Filter (EKF) is widely used, it has shortcomings in that the computational complexity grows in proportion to the square of the number of features. This prohibits EKF-SLAM from operating in real time and makes it unfeasible in large environments where many features exist. This paper presents an algorithm which reduces the computational complexity of EKF-SLAM by using topological information (TI) extracted through a thinning process. The global map can be divided into local areas using the nodes of a thinning-based topological map. SLAM is then performed in local instead of global areas. Experimental results for various environments show that the performance and efficiency of the proposed EKF-SLAM/TI scheme are excellent.

• 전기학회논문지
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• 제58권4호
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• pp.817-823
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• 2009

This paper proposes a robust method for mapping of a caterpillar-type mobile robot which inherently has uncertainty in its modeling by compensating for the estimated pose error of the robot. In general, a caterpillar type robot is difficult to model, which results in inaccuracy in Simultaneous Localization And Mapping(SLAM). To enhance the robustness of the SLAM for a caterpillar-type mobile robot, we factorize the SLAM posterior, where we used particle filter to estimate the position of the robot and Extended Kalman Filter(EKF) to map the environment. The simulation results show the effectiveness and robustness of the proposed method for mapping.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2011년도 춘계학술대회 논문집
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• pp.85-86
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• 2011

• 한국지능시스템학회논문지
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• 제17권4호
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• pp.506-510
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• 2007

전자나침반(DMC)은 실내의 전자기적 요소나 강한 자성체 건물구조에서는 쉽게 방해를 받던 나침반보다 실내에서 간섭에 강한 특징을 가지고 있다. 그리고 초음파 센서는 물체와의 거리를 계산해 줄뿐만 아니라 값싼 센서로서 경제적인 이점을 가지고 있어 Simultaneous Localization and Mapping(SLAM)에서 많이 사용하고 있다. 본 논문에서는 자율 이동 로봇의 구동에서 전자나침반과 초음파 센서를 이용한 SLAM의 구현에 대해 연구하였다. 로봇의 특성상 한정된 센싱 데이터만으로 방향과 위치를 파악하고 그 데이터 값으로 가능한 빠르게 위치 측정을 하여야 한다. 그러므로 자율 이동 로봇에서의 SLAM 적용함으로 위치측정의 구현과 지도 작성을 수행한다. 그리고 SLAM 구현상의 주된 연구 중의 하나인 Kid Napping 문제에 중점을 두고 연구한다. 특히, 위치 측정의 구현을 수행하기 위한 데이터의 센싱 방법으로 초음파 센서를 사용하였고 비슷한 위치의 데이터 값이 주어지거나 사전 정보 없는 상태에서는 로봇의 상태를 파악하기 위해서 전자 나침반을 사용하였다. 그래서 자율 이동 로봇의 위치를 정확하게 측정하기 위해서 활용하였다.

• 반도체디스플레이기술학회지
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• 제22권1호
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• pp.6-10
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• 2023

In this paper, we proposed a software framework structure to apply ORB-SLAM, the most representative of SLAM algorithms, so that map creation and location estimation technology can be applied through tethered AR glasses. Since tethered AR glasses perform only the role of an input/output device, the processing of camera and sensor data and the generation of images to be displayed through the optical display module must be performed through the host. At this time, an Android-based mobile device is adopted as the host. Therefore, the major libraries required for the implementation of AR contents for AR glasses were hierarchically organized, and spatial recognition and location estimation functions using SLAM were verified.

• 제어로봇시스템학회논문지
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• 제22권4호
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• pp.301-306
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• 2016

This paper presents the results of a study for robust self-localization and indoor slam using external cameras (such as a CCTV) and odometry of mobile robot. First, a position of mobile robot was estimated by using maker and odometry. This data was then fused with camera data and odometry data using an extended kalman filter. Finally, indoor slam was realized by applying the proposed method. This was demonstrated in the actual experiment.

• 전자공학회논문지SC
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• 제48권1호
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• pp.55-60
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• 2011

이동로봇의 동시 위치인식 및 지도작성 (Simultaneous Localization And Mapping, SLAM) 에서 가장 기본이 되는 알고리즘은 확장 칼만 필터 SLAM(Extended Kalman Filter SLAM, EKF-SLAM)이다. 하지만 칼만 필터를 사용할 때, 시스템 설계자는 외부 입력에 대한 시스템적 특성과 외부 노이즈의 확률적 모델을 알고 있어야 하나, 실제 환경에서는 이를 정확히 파악할 수 없는 한계가 있다. 이에, 칼만 필터를 불확실성이 심한 실제 환경에 적용할 경우 내부 변수의 변화에 민감하게 반응하거나, 필터의 수학적 일관성이 지켜지지 않거나 또는 부정확한 상태 변수값을 추정하기도 한다. 이에 비해 $H_{\infty}$ 필터는 외부 모델에 대한 상세한 정보가 없을지라도 강인하게 상태를 예측할 수 있다는 장점을 가지고 있다. 본 논문에서는 이러한 $H_{\infty}$ 필터의 특성이 이용로봇의 SLAM 알고리즘의 성능 향상에 도움이 될 것이라는 아이디어에 착안하여 $H_{\infty}$ 필터에 가번한 SLAM 알고리즘을 제안하고 이를 모의 실험에 적용해 보았다. 이를 통해 불확실성이 큰 환경에서는 제안된 알고리즘이 기존의 EKF-SLAM에 비해 다소 우수한 예측 성능을 보임을 확인할 수 있었다.