A critique of current developments in simultaneous localization and mapping

Huang, Shoudong; Dissanayake, Gamini

doi:10.1177/1729881416669482

Cited by 60 publications

(43 citation statements)

References 96 publications

(167 reference statements)

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“…In [85], a brief survey of the most common filter-based estimation techniques is given with a list of the pros and cons of each main paradigm. An interesting take on the SLAM is offered by Dissanayake et al in [86], and extended in [87], where the observability of SLAM, its convergence properties, its consistency as well as its computational efficiency are discussed. In [7], three estimation techniques are reviewed: EKF-SLAM, PF-SLAM and graphbased SLAM.…”

Section: Relevant Surveys and Existing Data Setsmentioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

666

366

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Abstract-In this article, we propose a survey of the Simultaneous Localization And Mapping field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multi-vehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various largescale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

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Section: Relevant Surveys and Existing Data Setsmentioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

666

366

View full text Add to dashboard Cite

show abstract

“…Özellikle Cadena vd. [33] ile Huang ve Dissanayake [35] tarafından gelecek çalışmalarında öngörüldüğü üzere sonraki EZKH araştırmaları içerisinde gerçek zamanlı (realtime) testlerle önerilen yöntemin hesaplama maliyetlerini daha net ve ayrıntılı ortaya koymak mümkün olabilir. …”

Section: Sonuçlar Ve Tartişmalar (Results and Discussion)unclassified

“…Bunlardan Huang vd. [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.…”

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 last assumption is generally not valid, especially for problems with high nonlinearity. Its adversarial effects have been observed and formally studied in the SLAM literature [15]. However, for applications with accurate robot heading estimate (linear dynamics) and high-frequency feedback on the changes in the states (central-limit theory), errors due to this assumption are often trivial.…”

Section: Flow-based Localization and Mappingmentioning

confidence: 99%

Concurrent Flow-Based Localization and Mapping in Time-Invariant Flow Fields

Song

Mohseni

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We present the concept of concurrent flow-based localization and mapping (FLAM) for autonomous field robots navigating within background flows. Different from the classical simultaneous localization and mapping (SLAM) problem, where the robot interacts with discrete features, FLAM utilizes the continuous flow fields as navigation references for mobile robots and provides flow field mapping capability with in-situ flow velocity observations. This approach is of importance to underwater vehicles in mid-depth oceans or aerial vehicles in GPS-denied atmospheric circulations. This article introduces the formulation of FLAM as a full SLAM solution motivated by the feature-based GraphSLAM framework. The performance of FLAM was demonstrated through simulation within artificial flow fields that represent typical geophysical circulation phenomena: a steady single-gyre flow field and a double-gyre flow field with unsteady turbulent perturbations. The results indicate that FLAM provides significant improvements in the robots' localization accuracy and a consistent approximation of the background flow field. It is also shown that FLAM leads to smooth robot trajectory estimates.

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A critique of current developments in simultaneous localization and mapping

Cited by 60 publications

References 96 publications

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

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

Concurrent Flow-Based Localization and Mapping in Time-Invariant Flow Fields

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