Autonomous navigation among large number of nearby landmarks using FastSLAM and EKF-SLAM - A comparative study

Monjazeb, A.; Sąsiadek, Jurek Z.; Necsulescu, D.

doi:10.1109/mmar.2011.6031375

Cited by 16 publications

(17 citation statements)

References 14 publications

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“…Apart from its implementation in SLAM, particle filters are also synonymous in path finding and searching methods for various other systems [23] [25]. FastSLAM was considered to be the first to model the non-linear processes and does not require Gaussian pose distribution in its method [3] [15]. However, the map does have its own set of independent Gaussians.…”

Section: Fast Slammentioning

confidence: 99%

A Brief Survey on SLAM Methods in Autonomous Vehicle

Takleh¹,

Bakar²,

Abdul-Rahman³

et al. 2018

IJET

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The overall purpose of this paper is to provide an introductory survey in the area of Simultaneous Localization and Mapping (SLAM) particularly its utilization in autonomous vehicle or more specifically in self-driving cars, especially after the release of commercial semiautonomous car like the Tesla vehicles as well as the Google Waymo vehicle. Before we begin diving into the concept of SLAM, we need to understand the importance of SLAM and problems that expand to the various methods developed by numerous researchers to solve it. Thus, in this paper we will start by giving the general concept behind SLAM, followed by sharing details of its different categories and the various methods that form the SLAM function in today's autonomous vehicles; which can solve the SLAM problem. These methods are the current trends that are widely focused in the research community in producing solutions to the SLAM problem; not only in autonomous vehicle but in the robotics field as well. Next, we will compare each of these methods in terms of its pros and cons before concluding the paper by looking at future SLAM challenges.

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Section: Fast Slammentioning

confidence: 99%

A Brief Survey on SLAM Methods in Autonomous Vehicle

Takleh¹,

Bakar²,

Abdul-Rahman³

et al. 2018

IJET

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“…[35], başlangıçtan günümüze EZKH çalışmaları üzerine tarama ve analizler yapmış, Monjazeb vd. [36] ise EKF ve Fast SLAM performanslarını karşılaştırmış, Rigatos [37] Araç modeli EZKH problemi için önem teşkil etmekte olup burada diferansiyel sürücü sistemli mobil iki tekerlekli bir robot kullanılmış ve denklemleri araç kinamatiği açısından ele alınmıştır. Aracın konumu Şekil 2'de gösterildiği gibi robot üzerinde hareket merkezi ve platform ağırlık merkezi aynı noktada olan bir düzlemde 3 serbestlik dereceli (x,y,θ) koordinatları ile tanımlanır [44].…”

Section: öNceki çAlışmalarunclassified

Eş zamanlı konum belirleme ve haritalama probleminde yeni bir durum tahmin yöntemi olarak parçacık akış filtresi

Duymaz¹,

Oğuz²,

Temeltaş³

2017

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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The Simultaneous Localization and Mapping (SLAM) problem, which emerged in the last quarter of the century, has been adapted for territorial, naval and aerial platforms starting from the year of 2000's and some parametric filter approaches such as Kalman Filter based Extended Kalman Filter and Distributed Kalman Filter, the stateestimation methods including nonparametric methods such as Particle Filter, some high level control aspiring, model or graphics-based and particularly image processing techniques has been used along with it. A strong need for performance analysis of the SLAM problem by classification can be mentioned, as it vary considerably in the platform, vehicle, sensor, and media type such as territorial, naval and aerial platforms. The particle flow filter, which put forward in 2009 for the first time, was particularly attractive due to its advantages such as high accuracy and fast convergence. In this research, a Particle Flow Filter based SLAM structure is given including mathematical bases/background of the filter, analysis, an autonomous ground vehicle and a sensor model, for the first time in the literature. According to the simulation results provided with the performance analysis of estimation under uncertainty tools/algorithms, although it has some computational complexity that may cause real time application concerns, the particle flow filter based SLAM performance is superior than other recursive state estimation approaches emerged before in the literature in terms of accuracy. Especially in the measurement environments with less uncertain sensors, it is preferable because it removes the problem of degeneration which arises in the particle filter structure.

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“…The samples are propagated through the high non-linearity of the system and more accurately approximate posterior belief of the robot to the second order Taylor series expansion. The approximation using UKF is more accurate compare to EKF which employs first order of Taylor series expansion specifically when motion and observation models are highly non-linear [7]. In UKF, sigma points (minimal set of sample points) around the mean are used in a deterministic sampling technique called unscented Transform [8].…”

Section: Unscented Hybridslammentioning

confidence: 99%

Autonomous mobile robot positioning using Unscented HybridSLAM

Monjazeb

Sąsiadek

Necsulescu

2013

2013 18th International Conference on Methods &Amp; Models in Automation &Amp; Robotics (MMAR)

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This paper addresses a novel approach to Simultaneous Localization and Mapping problem called Unscented HybridSLAM. Analyzing loop closing as one of the most important aspects of SLAM is the major interest of this study. Closing a loop by an autonomous mobile robot can be tricky even in the presence of a correct data association in the process, especially, when the loop is considerably large. The main challenge is to maintain a track of all correlations among landmarks and the mobile robot using Unscented HybridSLAM. The performance of proposed algorithm is evaluated with a loop closing scenario simulation, then, the results are discussed and compared with other algorithms.

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Autonomous navigation among large number of nearby landmarks using FastSLAM and EKF-SLAM - A comparative study

Cited by 16 publications

References 14 publications

A Brief Survey on SLAM Methods in Autonomous Vehicle

A Brief Survey on SLAM Methods in Autonomous Vehicle

Eş zamanlı konum belirleme ve haritalama probleminde yeni bir durum tahmin yöntemi olarak parçacık akış filtresi

Autonomous mobile robot positioning using Unscented HybridSLAM

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