• Title, Summary, Keyword: SLAM

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

  • Kim, Ryun-Seok;Choi, Hyuk-Doo;Kim, Eun-Tai
    • Journal of 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.

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

  • Oh, Jung-Suk;Sim, Kwee-Bo
    • Journal of 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).

Past and State-of-the-Art SLAM Technologies (SLAM 기술의 과거와 현재)

  • Song, Jae-Bok;Hwang, Seo-Yeon
    • Journal of Institute of Control, Robotics and Systems
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    • v.20 no.3
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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.

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 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.

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.

$H_{\infty}$ 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.

The Implementation of Graph-based SLAM Using General Graph Optimization (일반 그래프 최적화를 활용한 그래프 기반 SLAM 구현)

  • Ko, Nak-Yong;Chung, Jun-Hyuk;Jeong, Da-Bin
    • The Journal of the Korea institute of electronic communication sciences
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    • v.14 no.4
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    • pp.637-644
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    • 2019
  • This paper describes an implementation of a graph-based simultaneous localization and mapping(SLAM) method called the General Graph Optimization. The General Graph Optimization formulates the SLAM problem using nodes and edges. The nodes represent the location and attitude of a robot in time sequence, and the edge between the nodes depict the constraint between the nodes. The constraints are imposed by sensor measurements. The General Graph Optimization solves the problem by optimizing the performance index determined by the constraints. The implementation is verified using the measurement data sets which are open for test of various SLAM methods.

Implementation and Flight Test Performance Analysis of vSLAM Aided Integrated Navigation System for Rotary UAV (vSLAM 보조 통합항법시스템 구현 및 무인 회전익기를 이용한 비행시험 성능분석)

  • Yun, Suk-Chang;Lee, Byoung-Jin;Yun, Suk-Hwan;Lee, Young-Jae;Sung, Sang-Kyung
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.39 no.4
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    • pp.362-369
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    • 2011
  • In this paper, vSLAM aided integrated navigation system is implemented and performance analysis of the system is completed via flight test. The system can suppress divergence of position error of INS only system by updating vSLAM correction information when temporary GPS signal outage occurs in bad radio condition. In the flight test, integrated hardware containing GPS, IMU and camera is loaded under RC electric helicopter. Performance of the integrated navigation system is verified by comparing estimated position of INS/vSLAM system with that of INS only system.

A Position Estimation of Quadcopter Using EKF-SLAM (EKF-SLAM을 이용한 쿼드콥터의 위치 추정)

  • Cho, Youngwan;Hwang, Jaeyoung;Lee, Heejin
    • Journal of IKEEE
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    • v.19 no.4
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    • pp.557-565
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    • 2015
  • In this paper, a method for estimating the location of a quadcopter is proposed by applying an EKF-SLAM algorithm to its flight control, to autonomously control the flight of an unmanned quadcopter. The usefulness of this method is validated through simulations. For autonomously flying the unmanned quadcopter, an algorithm is required to estimate its accurate location, and various approaches exist for this. Among them, SLAM, which has seldom been applied to the quadcopter flight control, was applied in this study to simulate a system that estimates flight trajectories of the quadcopter.

The Reconstruction of Amplitude and Phase Images of SLAM by using Quadrature Detector (쿼드러쳐 검출기를 이용한 SLAM의 진폭과 위상 영상 복원)

  • 황기환
    • Proceedings of the Acoustical Society of Korea Conference
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    • pp.227.1-230
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    • 1998
  • 본 연구에서는 기존의 SLAM에서는 불가능한 진폭과 위상 정보를 동시에 검출할 수 있는 쿼드러춰 검출기를 설계 제작하여 SLAM을 구성하고 진폭과 위상영상을 복원하여 기존의 SALM 영상과 비교분석하였다. 실험을 위하여 동작주파수가 10MHz인 쿼드러춰 검출기를 제작하여 SLAM시스템을 구성하고 시편으로는 다른 패턴을 갖는 두 개의 층으로 이루어진 평면구조물을 알루미늄으로 가공하여 실험하였다. 실험결과 다층구조물에 대한 진폭과 위상 영상을 복원할 수 있었고 기존의 SLAM 영상과 비교하여 양호한 분해능과 콘트라스트를 나타냈으며 특히 기존의 방법으로는 얻을 수 없었던 위상영상을 얻을 수 있었다.

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