• 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).

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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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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An Improved FastSLAM Algorithm using Fitness Sharing Technique (적합도 공유 기법을 적용한 향상된 FastSLAM 알고리즘)

  • Kwon, Oh-Sung;Hyeon, Byeong-Yong;Seo, Ki-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.22 no.4
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    • pp.487-493
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    • 2012
  • SLAM(Simultaneous Localization And Mapping) is a technique used by robots and autonomous vehicles to build up a map within an unknown environment and estimate a place of robot. FastSLAM(A Factored Solution to the SLAM) is one of representative method of SLAM, which is based on particle filter and extended Kalman filter. However it is suffered from loss of particle diversity. In this paper, new approach using fitness sharing is proposed to supplement loss of particle diversity, compared and analyzed with existing methods.

The Motion Estimation of Caterpilla-type Mobile Robot Using Robust SLAM (강인한 SLAM을 이용한 무한궤도형 이동로봇의 모션 추정)

  • Byun, Sung-Jae;Lee, Suk-Gyu;Park, Ju-Hyun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.4
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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.

$H_{\infty}$ Filter Based Robust Simultaneous Localization and Mapping for Mobile Robots (이동로봇을 위한 $H_{\infty}$ 필터 기반의 강인한 동시 위치인식 및 지도작성 구현 기술)

  • Jeon, Seo-Hyun;Lee, Keon-Yong;Doh, Nakju Lett
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.48 no.1
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    • pp.55-60
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    • 2011
  • The most basic algorithm in SLAM(Simultaneous Localization And Mapping) technique of mobile robots is EKF(Extended Kalman Filter) SLAM. However, it requires prior information of characteristics of the system and the noise model which cannot be estimated in accurate. By this limit, Kalman Filter shows the following behaviors in a highly uncertain environment: becomes too sensitive to internal parameters, mathematical consistency is not kept, or yields a wrong estimation result. In contrast, $H_{\infty}$ filter does not requires a prior information in detail. Thus, based on a idea that $H_{\infty}$ filter based SLAM will be more robust than the EKF-SLAM, we propose a framework of $H_{\infty}$ filter based SLAM and show that suggested algorithm shows slightly better result man me EKF-SLAM in a highly uncertain environment.

EKF SLAM-based Camera Tracking Method by Establishing the Reference Planes (기준 평면의 설정에 의한 확장 칼만 필터 SLAM 기반 카메라 추적 방법)

  • Nam, Bo-Dam;Hong, Hyun-Ki
    • Journal of Korea Game Society
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    • v.12 no.3
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    • pp.87-96
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    • 2012
  • This paper presents a novel EKF(Extended Kalman Filter) based SLAM(Simultaneous Localization And Mapping) system for stable camera tracking and re-localization. The obtained 3D points by SLAM are triangulated using Delaunay triangulation to establish a reference plane, and features are described by BRISK(Binary Robust Invariant Scalable Keypoints). The proposed method estimates the camera parameters from the homography of the reference plane when the tracking errors of EKF SLAM are much accumulated. Using the robust descriptors over sequence enables us to re-localize the camera position for matching over sequence even though the camera is moved abruptly.

EKF-based SLAM Using Sonar Salient Feature and Line Feature for Mobile Robots (이동로봇을 위한 Sonar Salient 형상과 선 형상을 이용한 EKF 기반의 SLAM)

  • Heo, Young-Jin;Lim, Jong-Hwan;Lee, Se-Jin
    • Journal of the Korean Society for Precision Engineering
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    • v.28 no.10
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    • pp.1174-1180
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    • 2011
  • Not all line or point features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity because it is difficult to determine the correspondence of line or point features with previously registered feature. Confused line and point features in cluttered environments leads to poor SLAM performance. We introduce a sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The reliable line feature is expressed by its end points and engaged togather in EKF SLAM to overcome the geometric limits and maintain the map consistency. Experimental results demonstrate the validity and robustness of the proposed method.

Symmetrical model based SLAM : M-SLAM (대칭모형 기반 SLAM : M-SLAM)

  • Oh, Jung-Suk;Sim, Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.4
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    • pp.463-468
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    • 2010
  • The mobile robot which accomplishes a work in explored region does not know location information of surroundings. Traditionally, simultaneous localization and mapping(SLAM) algorithms solve the localization and mapping problem in explored regions. Among the several SLAM algorithms, the EKF (Extended Kalman Filter) based SLAM is the scheme most widely used. The EKF is the optimal sensor fusion method which has been used for a long time. The odometeric error caused by an encoder can be compensated by an EKF, which fuses different types of sensor data with weights proportional to the uncertainty of each sensor. In many cases the EKF based SLAM requires artificially installed features, which causes difficulty in actual implementation. Moreover, the computational complexity involved in an EKF increases as the number of features increases. And SLAM is a weak point of long operation time. Therefore, this paper presents a symmetrical model based SLAM algorithm(called M-SLAM).

Unmanned Aerial Vehicle Recovery Using a Simultaneous Localization and Mapping Algorithm without the Aid of Global Positioning System

  • Lee, Chang-Hun;Tahk, Min-Jea
    • International Journal of Aeronautical and Space Sciences
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    • v.11 no.2
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    • pp.98-109
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    • 2010
  • This paper deals with a new method of unmanned aerial vehicle (UAV) recovery when a UAV fails to get a global positioning system (GPS) signal at an unprepared site. The proposed method is based on the simultaneous localization and mapping (SLAM) algorithm. It is a process by which a vehicle can build a map of an unknown environment and simultaneously use this map to determine its position. Extensive research on SLAM algorithms proves that the error in the map reaches a lower limit, which is a function of the error that existed when the first observation was made. For this reason, the proposed method can help an inertial navigation system to prevent its error of divergence with regard to the vehicle position. In other words, it is possible that a UAV can navigate with reasonable positional accuracy in an unknown environment without the aid of GPS. This is the main idea of the present paper. Especially, this paper focuses on path planning that maximizes the discussed ability of a SLAM algorithm. In this work, a SLAM algorithm based on extended Kalman filter is used. For simplicity's sake, a blimp-type of UAV model is discussed and three-dimensional pointed-shape landmarks are considered. Finally, the proposed method is evaluated by a number of simulations.