• Title/Summary/Keyword: simultaneous localization and mapping (SLAM)

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Simultaneous Localization and Mapping of Mobile Robot using Digital Magnetic Compass and Ultrasonic Sensors (전자 나침반과 초음파 센서를 이용한 이동 로봇의 Simultaneous Localization and Mapping)

  • Kim, Ho-Duck;Seo, Sang-Wook;Jang, In-Hun;Sim, Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.17 no.4
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    • pp.506-510
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    • 2007
  • Digital Magnetic Compass(DMC) has a robust feature against interference in the indoor environment better than compass which is easily disturbed by electromagnetic sources or large ferromagnetic structures. Ultrasonic Sensors are cheap and can give relatively accurate range readings. So they ate used in Simultaneous Localization and Mapping(SLAM). In this paper, we study the Simultaneous Localization and Mapping(SLAM) of mobile robot in the indoor environment with Digital Magnetic Compass and Ultrasonic Sensors. Autonomous mobile robot is aware of robot's moving direction and position by the restricted data. Also robot must localize as quickly as possible. And in the moving of the mobile robot, the mobile robot must acquire a map of its environment. As application for the Simultaneous Localization and Mapping(SLAM) on the autonomous mobile robot system, robot can find the localization and the mapping and can solve the Kid Napping situation for itself. Especially, in the Kid Napping situation, autonomous mobile robot use Ultrasonic sensors and Digital Magnetic Compass(DMC)'s data for moving. The robot is aware of accurate location By using Digital Magnetic Compass(DMC).

Depth-hybrid speeded-up robust features (DH-SURF) for real-time RGB-D SLAM

  • Lee, Donghwa;Kim, Hyungjin;Jung, Sungwook;Myung, Hyun
    • Advances in robotics research
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    • v.2 no.1
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    • pp.33-44
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    • 2018
  • This paper presents a novel feature detection algorithm called depth-hybrid speeded-up robust features (DH-SURF) augmented by depth information in the speeded-up robust features (SURF) algorithm. In the keypoint detection part of classical SURF, the standard deviation of the Gaussian kernel is varied for its scale-invariance property, resulting in increased computational complexity. We propose a keypoint detection method with less variation of the standard deviation by using depth data from a red-green-blue depth (RGB-D) sensor. Our approach maintains a scale-invariance property while reducing computation time. An RGB-D simultaneous localization and mapping (SLAM) system uses a feature extraction method and depth data concurrently; thus, the system is well-suited for showing the performance of the DH-SURF method. DH-SURF was implemented on a central processing unit (CPU) and a graphics processing unit (GPU), respectively, and was validated through the real-time RGB-D SLAM.

2D Indoor Map Building Scheme Using Ultrasonic Module (초음파 센서 모듈을 활용한 2D 실내 지도 작성 기법)

  • Ahn, Deock-hyeon;Kim, Nam-moon;Park, Ji-hye;Kim, Young-ok
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.41 no.8
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    • pp.986-994
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    • 2016
  • In this paper, we proposed ultrasonic radar module and fixed module for the 2D indoor map building and from each of the modules, we can see the possibilities, limitations and considerations. And finally show the result of building actual 2D indoor map from the modules. Recently there are lots of works for the building indoor map by spotlight on the simultaneous localization and mapping (SLAM). And the LiDAR, ultrasonic, camera sensors are usually used for this work. Especially the LiDAR sensor have a higher resolution and wider detection range more than the ultrasonic sensor, but also there are limitation in the size of module, higher cost, much more throughput of processing data, and weaker to use in various indoor environment noises. So from these reasons, in this paper we could verify that proposed modules and schemes have a enough performance to build the 2D indoor map instead of using LiDAR and camera sensor with minimum number of ultrasonic sensors and less throughput of processing data.

Real-time Simultaneous Localization and Mapping (SLAM) for Vision-based Autonomous Navigation (영상기반 자동항법을 위한 실시간 위치인식 및 지도작성)

  • Lim, Hyon;Lim, Jongwoo;Kim, H. Jin
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.39 no.5
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    • pp.483-489
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    • 2015
  • In this paper, we propose monocular visual simultaneous localization and mapping (SLAM) in the large-scale environment. The proposed method continuously computes the current 6-DoF camera pose and 3D landmarks position from video input. The proposed method successfully builds consistent maps from challenging outdoor sequences using a monocular camera as the only sensor. By using a binary descriptor and metric-topological mapping, the system demonstrates real-time performance on a large-scale outdoor dataset without utilizing GPUs or reducing input image size. The effectiveness of the proposed method is demonstrated on various challenging video sequences.

Real-Time Mapping of Mobile Robot on Stereo Vision (스테레오 비전 기반 이동 로봇의 실시간 지도 작성 기법)

  • Han, Cheol-Hun;Sim, Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.1
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    • pp.60-65
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    • 2010
  • This paper describes the results of 2D mapping, feature detection and matching to create the surrounding environment in the mounted stereo camera on Mobile robot. Extract method of image's feature in real-time processing for quick operation uses the edge detection and Sum of Absolute Difference(SAD), stereo matching technique can be obtained through the correlation coefficient. To estimate the location of a mobile robot using ZigBee beacon and encoders mounted on the robot is estimated by Kalman filter. In addition, the merged gyro scope to measure compass is possible to generate map during mobile robot is moving. The Simultaneous Localization and Mapping (SLAM) of mobile robot technology with an intelligent robot can be applied efficiently in human life would be based.

Simultaneous Localization and Mapping of Mobile Robot using Digital Magnetic Compass and Ultrasonic Sensors (전자 나침반과 초음파 센서를 이용한 이동 로봇의 Simultaneous Localization and Mapping)

  • Kim, Ho-Deok;Lee, Hae-Gang;Seo, Sang-Uk;Jang, In-Hun;Sim, Gwi-Bo
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.37-40
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    • 2007
  • Digital Magnetic Compass(DMC)는 실내의 전자기적 요소나 강한 자성체 건물구조에서는 쉽게 방해를 받던 Compass보다 실내에서 간섭에 강한 특징을 가지고 있다. 그리고 적외선 센서와 초음파 센서는 서로 물체와의 거리를 보완적으로 계산해 줄뿐만 아니라 값싼 센서로서 경제적인 이점을 가지고 있어 Simultaneous Localization and Mapping(SLAM)에서 많이 사용하고 있다. 본 논문에서는 자율 이동 로봇의 구동에서 Digital Magnetic Compass(DMC)와 Ultrasonic Sensors을 이용한 SLAM의 구현에 대해 연구하였다. 로봇의 특성상 한정된 Sensing 데이터만으로 방향과 위치를 파악하고 그 데이터 값으로 가능한 빠르게 Localization을 하여야 한다. 그러므로 자율 이동 로봇에서의 SLAM 적용함으로 Localization 구현과 Mapping을 수행하고 SLAM 구현상의 주된 연구 중의 하나인 Kid Napping 문제에 중점을 두고 연구한다. 특히, Localization 구현을 수행을 위한 데이터의 Sensing 방법으로 적외선 센서와 초음파 센서를 같이 사용하였고 비슷한 위치의 데이터 값이 주어지거나 사전 정보 없는 상태에서는 로봇의 상태를 파악하기 위해서 DMC을 같이 사용하여 더 정확한 위치를 측정에 활용하였다.

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Mapless Navigation Based on DQN Considering Moving Obstacles, and Training Time Reduction Algorithm (이동 장애물을 고려한 DQN 기반의 Mapless Navigation 및 학습 시간 단축 알고리즘)

  • Yoon, Beomjin;Yoo, Seungryeol
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.3
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    • pp.377-383
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    • 2021
  • Recently, in accordance with the 4th industrial revolution, The use of autonomous mobile robots for flexible logistics transfer is increasing in factories, the warehouses and the service areas, etc. In large factories, many manual work is required to use Simultaneous Localization and Mapping(SLAM), so the need for the improved mobile robot autonomous driving is emerging. Accordingly, in this paper, an algorithm for mapless navigation that travels in an optimal path avoiding fixed or moving obstacles is proposed. For mapless navigation, the robot is trained to avoid fixed or moving obstacles through Deep Q Network (DQN) and accuracy 90% and 93% are obtained for two types of obstacle avoidance, respectively. In addition, DQN requires a lot of learning time to meet the required performance before use. To shorten this, the target size change algorithm is proposed and confirmed the reduced learning time and performance of obstacle avoidance through simulation.

Experiments of Unmanned Underwater Vehicle's 3 Degrees of Freedom Motion Applied the SLAM based on the Unscented Kalman Filter (무인 잠수정 3자유도 운동 실험에 대한 무향 칼만 필터 기반 SLAM기법 적용)

  • Hwang, A-Rom;Seong, Woo-Jae;Jun, Bong-Huan;Lee, Pan-Mook
    • Journal of Ocean Engineering and Technology
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    • v.23 no.2
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    • pp.58-68
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    • 2009
  • The increased use of unmanned underwater vehicles (UUV) has led to the development of alternative navigational methods that do not employ acoustic beacons and dead reckoning sensors. This paper describes a simultaneous localization and mapping (SLAM) scheme that uses range sonars mounted on a small UUV. A SLAM scheme is an alternative navigation method for measuring the environment through which the vehicle is passing and providing the relative position of the UUV. A technique for a SLAM algorithm that uses several ranging sonars is presented. This technique utilizes an unscented Kalman filter to estimate the locations of the UUV and surrounding objects. In order to work efficiently, the nearest neighbor standard filter is introduced as the data association algorithm in the SLAM for associating the stored targets returned by the sonar at each time step. The proposed SLAM algorithm was tested by experiments under various three degrees of freedom motion conditions. The results of these experiments showed that the proposed SLAM algorithm was capable of estimating the position of the UUV and the surrounding objects and demonstrated that the algorithm will perform well in various environments.

Design of Multiple Floors Autonomous Navigation System Based On ROS Enabled Mobile Robots (ROS 기반 모바일 로봇을위한 다중 층 자율 주행 시스템 설계)

  • Ahmed, Hamdi A.;Jang, Jong-Wook
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.55-57
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    • 2018
  • In Simultaneous Localization and Mapping (SLAM), the robot acquire its map of environment while simultaneously localize itself relative to the map. Now a day, a map acquired by the mobile robots limit to specific area, in an indoor environment and cannot able to navigate autonomous between different floors. We propose a design that could able to overcome this issue in order to navigate multiple floors with one end goal mission to a target destination in the course of autonomous navigation. In this research, we consider all the floors have identical structural arrangement. Internet of Things (IoT) playing crucial role in bridging between "things" and Robot Operating System (ROS) enabled mobile robots.

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Cloud Based Simultaneous Localization and Mapping with Turtlebot3 (Turtlebot3을 사용한 클라우드 기반 동시 로컬라이제이션 및 매핑)

  • Ahmed, Hamdi A.;Jang, Jong-Wook
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.241-243
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    • 2018
  • In this paper, in Simultaneous localization and mapping (SLAM), the robot acquire its map of environment while simultaneously localizing itself relative to the map. Cloud based SLAM, allows us to optimizing resource and data sharing like map of the environment, which allows us, as one of shared available online map. Doing so, unless we add or remove significant change in our environment, the essence of rebuilding new environmental map are omitted to new mobile robot added to the environment. As result, the requirement of additional sensor are curtailed.

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