Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Cadena, César; Carlone, Luca; Carrillo, Henry; Latif, Yasir; Scaramuzza, Davide; Neira, José; Reid, Ian; Leonard, John J.

doi:10.1109/tro.2016.2624754

Cited by 3,114 publications

(1,941 citation statements)

References 239 publications

Supporting

Mentioning

1,919

Contrasting

Unclassified

Order By: Relevance

“…A quick panorama has been made 10 years ago in [4] and [5]. Readers looking for an initiation to the global SLAM problem can also refer to [6] and [7] for a comprehensive introduction to the topic and to [8] for an extensive up-to-date review of the current challenges in SLAM.…”

Section: The Slam Problemmentioning

confidence: 99%

“…While this survey will also explore place recognition as it is an important aspect of SLAM, we will focus on its application to autonomous vehicle and on the maturity of existing approaches. Already mentioned before is the considerable work of Cadena et al in [8] where the SLAM topic is reviewed as a whole. Some aspects, not necessarily applicable yet in autonomous driving, will not be covered here (active SLAM, for instance).…”

Section: Relevant Surveys and Existing Data Setsmentioning

confidence: 99%

See 1 more Smart Citation

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

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

700

366

View full text Add to dashboard Cite

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.

show abstract

Section: The Slam Problemmentioning

confidence: 99%

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.

700

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

“…Cadena vd. [33], geçmiş çalışmalar kadar gelecekteki potansiyel EZKH çalışmalarına da projeksiyonda bulunmuşlardır [34]. Bunlardan Huang vd.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Current state of the art methods for depth from monocular view tends to use motion, and especially structure from motion, and most algorithms do not rely on deep learning (Cadena et al, 2016, Mur-Artal and Tardos, 2016, Klein and Murray, 2007, Pizzoli et al, 2014. Prior knowledge w.r.t.…”

Section: Depth Inferencementioning

confidence: 99%

End-to-End Depth From Motion With Stabilized Monocular Videos

Pinard¹,

Chevalley²,

Manzanera³

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:We propose a depth map inference system from monocular videos based on a novel dataset for navigation that mimics aerial footage from gimbal stabilized monocular camera in rigid scenes. Unlike most navigation datasets, the lack of rotation implies an easier structure from motion problem which can be leveraged for different kinds of tasks such as depth inference and obstacle avoidance. We also propose an architecture for end-to-end depth inference with a fully convolutional network. Results show that although tied to camera inner parameters, the problem is locally solvable and leads to good quality depth prediction.

show abstract

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Cited by 3,114 publications

References 239 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

End-to-End Depth From Motion With Stabilized Monocular Videos

Contact Info

Product

Resources

About