• Title/Summary/Keyword: simultaneous localization and mapping

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Approaches to Probabilistic Localization and Tracking for Autonomous Mobility Robot in Unknown Environment (미지환경에서 무인이동체의 자율주행을 위한 확률기반 위치 인식과 추적 방법)

  • Jin, Taeseok
    • 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.

Mobile Robot Localization and Mapping using a Gaussian Sum Filter

  • Kwok, Ngai Ming;Ha, Quang Phuc;Huang, Shoudong;Dissanayake, Gamini;Fang, Gu
    • International Journal of Control, Automation, and Systems
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    • v.5 no.3
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    • pp.251-268
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    • 2007
  • A Gaussian sum filter (GSF) is proposed in this paper on simultaneous localization and mapping (SLAM) for mobile robot navigation. In particular, the SLAM problem is tackled here for cases when only bearing measurements are available. Within the stochastic mapping framework using an extended Kalman filter (EKF), a Gaussian probability density function (pdf) is assumed to describe the range-and-bearing sensor noise. In the case of a bearing-only sensor, a sum of weighted Gaussians is used to represent the non-Gaussian robot-landmark range uncertainty, resulting in a bank of EKFs for estimation of the robot and landmark locations. In our approach, the Gaussian parameters are designed on the basis of minimizing the representation error. The computational complexity of the GSF is reduced by applying the sequential probability ratio test (SPRT) to remove under-performing EKFs. Extensive experimental results are included to demonstrate the effectiveness and efficiency of the proposed techniques.

A Study on Fisheye Lens based Features on the Ceiling for Self-Localization (실내 환경에서 자기위치 인식을 위한 어안렌즈 기반의 천장의 특징점 모델 연구)

  • Choi, Chul-Hee;Choi, Byung-Jae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.21 no.4
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    • pp.442-448
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    • 2011
  • There are many research results about a self-localization technique of mobile robot. In this paper we present a self-localization technique based on the features of ceiling vision using a fisheye lens. The features obtained by SIFT(Scale Invariant Feature Transform) can be used to be matched between the previous image and the current image and then its optimal function is derived. The fisheye lens causes some distortion on its images naturally. So it must be calibrated by some algorithm. We here propose some methods for calibration of distorted images and design of a geometric fitness model. The proposed method is applied to laboratory and aile environment. We show its feasibility at some indoor environment.

Omni-directional Visual-LiDAR SLAM for Multi-Camera System (다중 카메라 시스템을 위한 전방위 Visual-LiDAR SLAM)

  • Javed, Zeeshan;Kim, Gon-Woo
    • The Journal of Korea Robotics Society
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    • v.17 no.3
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    • pp.353-358
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    • 2022
  • Due to the limited field of view of the pinhole camera, there is a lack of stability and accuracy in camera pose estimation applications such as visual SLAM. Nowadays, multiple-camera setups and large field of cameras are used to solve such issues. However, a multiple-camera system increases the computation complexity of the algorithm. Therefore, in multiple camera-assisted visual simultaneous localization and mapping (vSLAM) the multi-view tracking algorithm is proposed that can be used to balance the budget of the features in tracking and local mapping. The proposed algorithm is based on PanoSLAM architecture with a panoramic camera model. To avoid the scale issue 3D LiDAR is fused with omnidirectional camera setup. The depth is directly estimated from 3D LiDAR and the remaining features are triangulated from pose information. To validate the method, we collected a dataset from the outdoor environment and performed extensive experiments. The accuracy was measured by the absolute trajectory error which shows comparable robustness in various environments.

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.

Semantic Visual Place Recognition in Dynamic Urban Environment (동적 도시 환경에서 의미론적 시각적 장소 인식)

  • Arshad, Saba;Kim, Gon-Woo
    • The Journal of Korea Robotics Society
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    • v.17 no.3
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    • pp.334-338
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    • 2022
  • In visual simultaneous localization and mapping (vSLAM), the correct recognition of a place benefits in relocalization and improved map accuracy. However, its performance is significantly affected by the environmental conditions such as variation in light, viewpoints, seasons, and presence of dynamic objects. This research addresses the problem of feature occlusion caused by interference of dynamic objects leading to the poor performance of visual place recognition algorithm. To overcome the aforementioned problem, this research analyzes the role of scene semantics in correct detection of a place in challenging environments and presents a semantics aided visual place recognition method. Semantics being invariant to viewpoint changes and dynamic environment can improve the overall performance of the place matching method. The proposed method is evaluated on the two benchmark datasets with dynamic environment and seasonal changes. Experimental results show the improved performance of the visual place recognition method for vSLAM.

Autonomous Navigation System of an Unmanned Aerial Vehicle for Structural Inspection (무인 구조물 검사를 위한 자율 비행 시스템)

  • Jung, Sungwook;Choi, Duckyu;Song, Seungwon;Myung, Hyun
    • The Journal of Korea Robotics Society
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    • v.16 no.3
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    • pp.216-222
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    • 2021
  • Recently, various robots are being used for the purpose of structural inspection or safety diagnosis, and their needs are also rising rapidly. Among the structural inspection using robots, a lot of researches has recently been conducted on inspection of various facilities and structures using an unmanned aerial vehicle (UAV). However, since GNSS (Global Navigation Satellite System) signals cannot be received in an environment near or below structures, the operation of UAVs has been done manually. For a stable autonomous flight without GNSS signals, additional technologies are required. This paper proposes the autonomous flight system for structural inspection consisting of simultaneous localization and mapping (SLAM), path planning, and controls. The experiments were conducted on an actual large bridge to verify the feasibility of the system, and especially the performance of the proposed SLAM algorithm was compared through comparative analysis with the state-of-the-art algorithms.

Vision-based Localization for AUVs using Weighted Template Matching in a Structured Environment (구조화된 환경에서의 가중치 템플릿 매칭을 이용한 자율 수중 로봇의 비전 기반 위치 인식)

  • Kim, Donghoon;Lee, Donghwa;Myung, Hyun;Choi, Hyun-Taek
    • Journal of Institute of Control, Robotics and Systems
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    • v.19 no.8
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    • pp.667-675
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    • 2013
  • This paper presents vision-based techniques for underwater landmark detection, map-based localization, and SLAM (Simultaneous Localization and Mapping) in structured underwater environments. A variety of underwater tasks require an underwater robot to be able to successfully perform autonomous navigation, but the available sensors for accurate localization are limited. A vision sensor among the available sensors is very useful for performing short range tasks, in spite of harsh underwater conditions including low visibility, noise, and large areas of featureless topography. To overcome these problems and to a utilize vision sensor for underwater localization, we propose a novel vision-based object detection technique to be applied to MCL (Monte Carlo Localization) and EKF (Extended Kalman Filter)-based SLAM algorithms. In the image processing step, a weighted correlation coefficient-based template matching and color-based image segmentation method are proposed to improve the conventional approach. In the localization step, in order to apply the landmark detection results to MCL and EKF-SLAM, dead-reckoning information and landmark detection results are used for prediction and update phases, respectively. The performance of the proposed technique is evaluated by experiments with an underwater robot platform in an indoor water tank and the results are discussed.

Loop Closure Detection Using Variational Autoencoder in Simultaneous Localization and Mapping (동시적 위치 추정 및 지도 작성에서 Variational Autoencoder 를 이용한 루프 폐쇄 검출)

  • Shin, Dong-Won;Ho, Yo-Sung
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2017.06a
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    • pp.250-253
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    • 2017
  • 본 논문에서는 동시적 위치 추정 및 지도 작성 (simultaneous localization and mapping)에서 루프 폐쇄 검출을 딥러닝 방법의 일종인 variational autoencoder 를 이용하여 수행하는 방법에 대해 살펴본다. Autoencoder 는 비감독 학습 방법의 일종으로 입력 영상이 신경망을 통과하여 얻은 출력 영상과 동일하도록 신경망을 학습시키는 모델이다. 이 때 autoencoder 중간의 병목 지역을 통과함에도 불구하고 입력과 동일한 영상을 계산해야 하는 제약조건이 있기 때문에 이는 차원 축소나 데이터 추상화의 목적으로 많이 사용된다. 여기서 한 단계 더 발전된 variational autoencoder 는 기존의 autoencoder 가 가진 단점인 입력 변수의 분포와 잠재 변수의 분포 사이에 상관관계가 없다는 단점을 해결하기 위해 Kullback-Leibler divergence 를 활용한 손실 함수를 정의하여 사용했다. 실험결과에서는 루프 폐쇄 검출에서 많이 사용되는 City-Centre 와 New College 데이터 집합을 사용하여 평가하였으며 루프 폐쇄 검출의 결과는 정밀도와 재현율을 계산하여 나타냈다.

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