• 한국지능시스템학회논문지
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• 제21권4호
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• pp.493-498
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• 2011

본 논문은 측정값의 우도를 기준으로 선택적인 측정값 적용을 통한 향상된 GraphSLAM을 제안하였다. GraphSLAM은 로봇의 이동 경로와 환경에 대한 지도를 전체 입력 데이터를 통해 추정한다. 그러나 잡음이 강한 환경에서 센서의 측정치가 부정확한 경우가 늘어나면, 전체 입력 데이터를 사용하는 GraphSLAM의 경우 정확성이 크게 떨어지게 된다. 그러므로 본 논문에서는 들어오는 센서의 측정값들을 선별하여 GraphSLAM에 적용하는 방법을 제안한다. 이 방법을 통해 잡음이 강한 환경에서 기존의 GraphSLAM보다 향상된 성능을 제공할 수 있다.

• 한국지능시스템학회논문지
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• 제20권4호
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• pp.463-468
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• 2010

미지의 영역에서 작업을 수행하고자 하는 이동로봇은 주변의 지도가 없을 뿐만 아니라 자신의 위치도 알 수 없다. 이러한 환경의 극복을 위해 가장 많이 쓰이는 방법이 SLAM(Simultaneous Localization And Mapping)이다. SLAM 분야에서 가장 많이 쓰이는 방법은 EKF (Extended Kalman Filter) 기반의 SLAM이다. 최적의 센서 융합 기법이지만 odometeric error 등을 보상하기 위해서는 복잡한 과정이 점차 증가하게 된다. 사람은 SLAM 방식을 이용하여 낯선 장소에서 마음속의 지도를 쉽게 작성하지만 로봇의 경우 SLAM을 수행하는 것은 매우 어렵고 시간이 오래 걸린다는 단점이 생기는 것이 다. 이러한 단점의 보완을 위하여 본 논문에서는 대칭모형 SLAM(M-SLAM)을 제안한다. M-SLAM은 대칭에 사용할 모형을 미리 정하고 센서로 받아들인 데이터를 모형과 비교하여 대칭된 모형을 맵에 적용시켜서 작업의 양을 줄이는 방법이다. M-SLAM은 적은 특징점을 이용하여 선택된 대칭 도형과의 유사성 판별을 이용하는 방법이므로 특징점이 적은 거리센서에 사용하기 적합한 특성을 가지고 있다고 할 수 있다. 특징점이 적어도 된다는 장점은 SLAM의 시간을 크게 줄여 줄수 있다.

• 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.

• 제어로봇시스템학회논문지
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• 제20권3호
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• pp.372-379
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• 2014

This paper surveys past and state-of-the-art SLAM technologies. The standard methods for solving the SLAM problem are the Kalman filter, particle filter, graph, and bundle adjustment-based methods. Kalman filters such as EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter) have provided successful results for estimating the state of nonlinear systems and integrating various sensor information. However, traditional EKF-based methods suffer from the increase of computation burden as the number of features increases. To cope with this problem, particle filter-based SLAM approaches such as FastSLAM have been widely used. While particle filter-based methods can deal with a large number of features, the computation time still increases as the map grows. Graph-based SLAM methods have recently received considerable attention, and they can provide successful real-time SLAM results in large urban environments.

• 로봇학회논문지
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• 제16권2호
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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.

• 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.

• 한국전자통신학회논문지
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• 제17권4호
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• pp.687-694
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• 2022

본 논문에서는 Visual 동시적 위치추정 및 지도작성(SLAM : Simultaneous Localization and Mapping)기술을 응용하여 실내에서 생성된 SLAM 맵을 기반으로 지정된 목적지에 물건을 배달하는 자율주행 차량 플랫폼을 제안하였다. 실내에서 SLAM 맵을 생성하기 위해 소형 자율주행 차량 플랫폼의 상단에 SLAM 맵 생성을 위한 심도 카메라를 설치하고 SLAM 맵 속에서의 정확한 위치추정을 하기 위해 추적 카메라를 장착하여 구현하였다. 또한, 목적지의 표찰을 인식하기 위해 합성곱 신경망(CNN : Convolutional neural network)을 사용하여 목적지에 정확하게 도착할 수 있도록 주행 알고리즘을 적용하여 설계하였다. 실내 배송 자율주행 차량을 실제로 제작하였고 SLAM 맵의 정확도 확인과 CNN을 통한 목적지 표찰 인식 실험을 수행하였다. 결과적으로 표찰 인식의 성공률을 향상시켜 구현한 실내 배송용 자율주행 차량의 활용 적합성 여부를 확인하였다.

• 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.

• 대한임베디드공학회논문지
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• 제19권1호
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• pp.65-71
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• 2024

An indirect visual SLAM takes raw image data and exploits geometric information such as key-points and line edges. Due to various environmental changes, SLAM performance may decrease. The main problem is caused by dynamic objects especially in highly crowded environments. In this paper, we propose a robust feature-based visual SLAM, building on ORB-SLAM, via multi-channel dynamic objects estimation. An optical flow and deep learning-based object detection algorithm each estimate different types of dynamic object information. Proposed method incorporates two dynamic object information and creates multi-channel dynamic masks. In this method, information on actually moving dynamic objects and potential dynamic objects can be obtained. Finally, dynamic objects included in the masks are removed in feature extraction part. As a results, proposed method can obtain more precise camera poses. The superiority of our ORB-SLAM was verified to compared with conventional ORB-SLAM by the experiment using KITTI odometry dataset.