Robustness to connectivity loss for collaborative mapping

Quraishi, Anwar; Cieslewski, Titus; Lynen, Simon; Siegwart, Roland

doi:10.1109/iros.2016.7759674

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Thus (Leung et al, 2011b;Leung et al, 2012) examine the conditions that allow distributed inference to reach the same result as a centralized equivalent approach. Another approach (Quraishi et al, 2016) leverages the progress in the field of distributed computing to improve the robustness to connectivity losses, while (Tuna et al, 2015) evaluates the use of Wireless Sensor Network-based communication which is less reliable and predictable, but offers a flexible architecture with self-organization capabilities.…”

Section: Network Topologymentioning

confidence: 99%

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Lajoie

Ramtoula

Wu³

et al. 2022

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Motivated by the tremendous progress, we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area’s current trends and promising research avenues.

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Section: Network Topologymentioning

confidence: 99%

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Lajoie

Ramtoula

Wu³

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Decentralized allocation schemes are more conducive to the distribution of tasks and data which compensates for the absence of a central server. Such pooling of computational resources may target specific modules such as correspondence detection [26] and global correspondences consistency checks [27], distributed inference [27,28] and map storage [29,30].…”

Section: Decentralized Allocation Schemesmentioning

confidence: 99%

Sharing visual-inertial data for collaborative decentralized simultaneous localization and mapping

Dubois

Eudes

Frémont

2022

Robotics and Autonomous Systems

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Building on the maturity of single-robot SLAM algorithms, collaborative SLAM has brought significant gains in terms of efficiency and robustness, but has also raised new challenges to cope with like informational, network and resource constraints. Several multi-robot frameworks have been coined for visual SLAM, ranging from highly-integrated and fully-centralized architectures to fully distributed and decentralized methods. However, many proposed architectures compromise the autonomy of the robots in fusing the data processed by the other agents to enhance their own estimation accuracy. In this paper, we propose three methods to share visual-inertial information, based on rigid, condensed and pruned visual-inertial packets. We also propose a common collaborative SLAM architecture to organize the computation, exchange and integration of such packets. We evaluated those methods on the EuRoC [1] dataset and on our custom dataset AirMuseum [2]. Experiments showed that the proposed methods allow the agents to build, exchange and integrate consistent visual-inertial packets, and improve their trajectory estimation accuracy up to several centimeters.

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Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Lajoie,

Ramtoula,

et al. 2021

Preprint

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Motivated by the tremendous progress we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multirobot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area's current trends and promising research avenues.

show abstract

Robustness to connectivity loss for collaborative mapping

Cited by 3 publications

References 16 publications

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Sharing visual-inertial data for collaborative decentralized simultaneous localization and mapping

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

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