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

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SLAM with Visually Salient Line Features in Indoor Hallway Environments (실내 복도 환경에서 선분 특징점을 이용한 비전 기반의 지도 작성 및 위치 인식)

  • An, Su-Yong;Kang, Jeong-Gwan;Lee, Lae-Kyeong;Oh, Se-Young
    • Journal of Institute of Control, Robotics and Systems
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    • v.16 no.1
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    • pp.40-47
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    • 2010
  • This paper presents a simultaneous localization and mapping (SLAM) of an indoor hallway environment using Rao-Blackwellized particle filter (RBPF) along with a line segment as a landmark. Based on the fact that fluent line features can be extracted around the ceiling and side walls of hallway using vision sensor, a horizontal line segment is extracted from an edge image using Hough transform and is also tracked continuously by an optical flow method. A successive observation of a line segment gives initial state of the line in 3D space. For data association, registered feature and observed feature are matched in image space through a degree of overlap, an orientation of line, and a distance between two lines. Experiments show that a compact environmental map can be constructed with small number of horizontal line features in real-time.

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.

EKF-based Simultaneous Localization and Mapping of Mobile Robot using Laser Corner Pattern Matching (레이저 코너 패턴의 매칭을 이용한 이동 로봇의 EKF 기반 SLAM)

  • Kim, Tae-Hyeong;Park, Tae-Hyoung
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.12
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    • pp.2094-2102
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    • 2016
  • In this paper, we propose an extended Kalman filter(EKF)-based simultaneous localization and mapping(SLAM) method using laser corner pattern matching for mobile robots. SLAM is one of the most important problems of mobile robot. However, existing method has the disadvantage of increasing the computation time, depending on the number of landmarks. To improve computation time, we produce the corner pattern using classified and detected corner points. After producing the corner patterns, it is estimated that mobile robot's global position by matching them. The estimated position is used as measurement model in the EKF. To evaluated proposed method, we preformed the experiments in the indoor environments. Experimental results of proposed method are shown to maintain an accuracy and decrease the computation time.

Visual SLAM using Local Bundle Optimization in Unstructured Seafloor Environment (국소 집단 최적화 기법을 적용한 비정형 해저면 환경에서의 비주얼 SLAM)

  • Hong, Seonghun;Kim, Jinwhan
    • The Journal of Korea Robotics Society
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    • v.9 no.4
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    • pp.197-205
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    • 2014
  • As computer vision algorithms are developed on a continuous basis, the visual information from vision sensors has been widely used in the context of simultaneous localization and mapping (SLAM), called visual SLAM, which utilizes relative motion information between images. This research addresses a visual SLAM framework for online localization and mapping in an unstructured seabed environment that can be applied to a low-cost unmanned underwater vehicle equipped with a single monocular camera as a major measurement sensor. Typically, an image motion model with a predefined dimensionality can be corrupted by errors due to the violation of the model assumptions, which may lead to performance degradation of the visual SLAM estimation. To deal with the erroneous image motion model, this study employs a local bundle optimization (LBO) scheme when a closed loop is detected. The results of comparison between visual SLAM estimation with LBO and the other case are presented to validate the effectiveness of the proposed methodology.

GraphSLAM Improved by Removing Measurement Outliers (측정 아웃라이어 제거를 통해 개선된 GraphSLAM)

  • Kim, Ryun-Seok;Choi, Hyuk-Doo;Kim, Eun-Tai
    • Journal of the Korean Institute of Intelligent Systems
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    • v.21 no.4
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    • pp.493-498
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    • 2011
  • This paper presents the GraphSLAM improved by selecting the measurement with respect to their likelihoods. GraphSLAM estimates the robot's path and map by utilizing the entire history of input data. However, GraphSLAM's performance suffers a lot from severely noisy measurements. In this paper, we present GraphSLAM improved by the selective measurement method. Thus the presented GraphSLAM provides higher performance compared with the standard GraphSLAM.

An Embedded Solution for Fast Navigation and Precise Positioning of Indoor Mobile Robots by Floor Features (바닥 특징점을 사용하는 실내용 정밀 고속 자율 주행 로봇을 위한 싱글보드 컴퓨터 솔루션)

  • Kim, Yong Nyeon;Suh, Il Hong
    • The Journal of Korea Robotics Society
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    • v.14 no.4
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    • pp.293-300
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    • 2019
  • In this paper, an Embedded solution for fast navigation and precise positioning of mobile robots by floor features is introduced. Most of navigation systems tend to require high-performance computing unit and high quality sensor data. They can produce high accuracy navigation systems but have limited application due to their high cost. The introduced navigation system is designed to be a low cost solution for a wide range of applications such as toys, mobile service robots and education. The key design idea of the system is a simple localization approach using line features of the floor and delayed localization strategy using topological map. It differs from typical navigation approaches which usually use Simultaneous Localization and Mapping (SLAM) technique with high latency localization. This navigation system is implemented on single board Raspberry Pi B+ computer which has 1.4 GHz processor and Redone mobile robot which has maximum speed of 1.1 m/s.

SLAM Aided GPS/INS/Vision Navigation System for Helicopter (SLAM 기반 GPS/INS/영상센서를 결합한 헬리콥터 항법시스템의 구성)

  • Kim, Jae-Hyung;Lyou, Joon;Kwak, Hwy-Kuen
    • Journal of Institute of Control, Robotics and Systems
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    • v.14 no.8
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    • pp.745-751
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    • 2008
  • This paper presents a framework for GPS/INS/Vision based navigation system of helicopters. GPS/INS coupled algorithm has weak points such as GPS blockage and jamming, while the helicopter is a speedy and high dynamical vehicle amenable to lose the GPS signal. In case of the vision sensor, it is not affected by signal jamming and also navigation error is not accumulated. So, we have implemented an GPS/INS/Vision aided navigation system providing the robust localization suitable for helicopters operating in various environments. The core algorithm is the vision based simultaneous localization and mapping (SLAM) technique. For the verification of the SLAM algorithm, we performed flight tests. From the tests, we confirm the developed system is robust enough under the GPS blockage. The system design, software algorithm, and flight test results are described.

SLAM of a Mobile Robot using Thinning-based Topological Information

  • Lee, Yong-Ju;Kwon, Tae-Bum;Song, Jae-Bok
    • International Journal of Control, Automation, and Systems
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    • v.5 no.5
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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.

Strategic Games for Particle Survival in Rao-Blackwellized Particle Filter for SLAM (Rao-Blackwellized 파티클 필터에서 파티클 생존을 위한 전략 게임)

  • Kwak, No-San;Kita, Nobuyuki;Yokoi, Kazuhito
    • The Journal of Korea Robotics Society
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    • v.4 no.2
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    • pp.97-104
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    • 2009
  • Recently, simultaneous localization and mapping (SLAM) approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, due to the usage of the accurate sensors, distinct particles which compensate one another are attenuated as the RBPF-SLAM continues. To avoid this particle depletion, we propose the strategic games to assign the particle's payoff which replaces the importance weight in the current RBPF-SLAM framework. From simulation works, we show that RBPF-SLAM with the strategic games is inconsistent in the pessimistic way, which is different from the existing optimistic RBPF-SLAM. In addition, first, the estimation errors with applying the strategic games are much less than those of the standard RBPF-SLAM, and second, the particle depletion is alleviated.

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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.