• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.437-438
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• 2019

본 논문에서는 ROS(Robot Operating System)기반으로한 로봇(Robot)에 레이저 거리 센서(LiDAR)를 설치하여 SLAM(Simultaneous Localization and Mapping : 동시적 위치 추적 지도 작성)기법으로 맵 정보를 습득하고, 저장하여 이를 기반으로 장애물과 건물의 실내 복도 안전하고 정확하게 순찰 할 수 있도록 하였다. 또한, 순찰 로봇(Robot)에 장착된 Raspberry카메라와 OpenCV 영상인식 기술을 이용하여 실시간 영상으로 실내 복도를 순찰하면서 사전에 설정된 특이사항이 있을 시 발견하고 기록하도록 시스템을 개발하였다.

• ETRI Journal
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• v.43 no.4
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• pp.617-629
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• 2021

Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed-loop online dense RGB-D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two-wheel differential-drive robot. It exhibits lightweight three-dimensional mapping, room-scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on-the-fly navigation while mapping.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.341-347
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• 2022

This paper presents a comparison result of two simultaneous localization and mapping (SLAM) algorithms for navigation that have been proposed in literature. The performances of Extended Kalman Filter (EKF) SLAM under Gaussian condition, FastSLAM algorithms using Rao-Blackwellised method for particle filtering are compared in terms of accuracy of state estimations for localization of a robot and mapping of its environment. The algorithms were run using the same type of robot on indoor environment. The results show that the Particle filter based FastSLAM has the better performance in terms of accuracy of localization and mapping. The experimental results are discussed and compared.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.135-142
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• 2023

Light detection and ranging (LiDAR) sensors have been most widely used in terrestrial robotic applications because they can provide dense and precise measurements of the surrounding environments. However, the reliability of LiDAR measurements can considerably vary due to the different reflectivities of laser beams to the reflecting surface materials. This study presents a robust LiDAR-based mapping method for the varying laser reflectivities in indoor environments using the framework of simultaneous localization and mapping (SLAM). The proposed method can minimize the performance degradations in the SLAM accuracy by checking and discarding potentially unreliable LiDAR measurements in the SLAM front-end process. The gaps in point-cloud maps created by the proposed approach are filled by a Gaussian process regression method. Experimental results with a mobile robot platform in an indoor environment are presented to validate the effectiveness of the proposed methodology.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.153-154
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• 2021

본 논문에서는 ROS(Robot Operating System)를 기반으로 한 로봇(Robot)에 LiDAR 센서를 설치하여 SLAM(Simultaneous Localization and Mapping) 기술인 동시적 위치 추적 지도 작성 기법을 이용하여 실내 맵 정보를 습득하고, 이를 기반으로 장애물과 건물 실내를 안전하고 정확하게 이동할 수 있도록 하였다. 또한 로봇에 자바에서 제공하는 개발 툴킷 Swing 및 AWT 라이브러리를 이용하여 GUI(Graphical User Interface)를 구현하였고 터치스크린을 장착하여 사용자가 원하는 제품을 선택하고 선택한 제품의 목적지를 습득한 맵을 토대로 좌표 값을 설정하여 ROS에서 지원하는 이동 프로세스를 실행시켜 목적지까지 경로를 설정하고 자율 주행하게 된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1372-1375
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• 2021

최근 산업 현장에서 많은 안전사고가 일어나고 있고, 현장 노동력의 부족으로 무인 로봇 시스템들을 도입하는 등 다양한 변화를 맞이하고 있다. 이에 차세대 자동화 시스템은 보다 유연하고 지능적이어야 한다. AGV(Automatic Guided Vehicle)의 경우 실시간으로 변하는 현장에 대응하기 어렵고, 새로운 어플리케이션에 대한 제품개발의 어려움이 따른다. 이에 대한 대안으로 AGV 인식 스택을 재구축하여 인간과 동일한 공간인식 능력을 갖춘 AMR(Autonomous Mobile Robot)이 대두되고 있다. 본 연구에서는 SLAM과 ROS를 이용하여 AMR의 기능을 구축하였다. YD Lidar 센서와 SLAM을 이용하여 주변 환경을 지도화 하여 로봇의 현재 위치를 파악할 수 있도록 제작하였고, 직접 지도상의 최적 경로를 파악하여 주변 장애물을 회피하며 주행할 수 있음을 확인하였다. DC 모터의 응답 특성에 따라 주행 속도, 조향각 등을 제어할 수 있도록 구현하였다.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.249-257
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• 2023

This paper discusses the increasing need for autonomous delivery robots due to the current growth in the delivery market, rising delivery fees, high costs of hiring delivery personnel, and the need for contactless services. Additionally, the cost of hardware and complex software systems required to build and operate autonomous delivery robots is high. To provide a low-cost alternative to this, this paper proposes a autonomous delivery robot platform using a low-cost sensor combination of 2D LIDAR, depth camera and tracking camera to replace the existing expensive 3D LIDAR. The proposed robot was developed using the RTAB-Map SLAM open source package for 2D mapping and overcomes the limitations of low-cost sensors by using the convex hull algorithm. The paper details the hardware and software configuration of the robot and presents the results of driving experiments. The proposed platform has significant potential for various industries, including the delivery and other industries.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.81-82
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• 2010

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.59-60
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• 2010

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.155-156
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• 2009