Decentralized Visual-Inertial Localization and Mapping on Mobile Devices for Augmented Reality

Sartipi, Kourosh; DuToit, Ryan C.; Cobar, Christopher B.; Roumeliotis, Stergios I.

doi:10.1109/iros40897.2019.8967804

Cited by 10 publications

(9 citation statements)

References 32 publications

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“…spotting of inter-robot correspondence and running global inference). Such approach has been implemented by several methods such as DDF-SAM [22], AUV-CSLAM [23] for underwater acoustic SLAM, Schuster et al [24] for dense stereo-visual collaborative SLAM, Dubois et al [5] and Sartipi et al [25] for visual-inertial CSLAM. Decentralized allocation schemes are more conducive to the distribution of tasks and data which compensates for the absence of a central server.…”

Section: Decentralized Allocation Schemesmentioning

confidence: 99%

“…Additionally, indirect correspondences are spotted through visual-structural correspondences. Instantaneous indirect correspondences, which only compares the robots' current observations, are mostly used for collaborative localization [25,36]. Retrospective indirect correspondences may be spotted between any interrobot pair of keyframes upon the reception of data from other robots.…”

Section: Matching and Merging Strategies 251 Inter-robot Corresponden...mentioning

confidence: 99%

“…Global consistency is then approached by mutually registering the locally consistent submaps. The sub-mapping approach has been used in decentralized collaborative SLAM: Schuster et al [24] make robots exchange dense stereo-visual point clouds registered through ICP, Sartipi et al [25] exchange and update current visualinertial submaps for collaborative localization and [66] exchange submaps associated to Truncated Signed Distance Functions for dense mapping.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Decentralized Allocation Schemesmentioning

confidence: 99%

Section: Matching and Merging Strategies 251 Inter-robot Corresponden...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Dubois

Eudes

Frémont

2022

Robotics and Autonomous Systems

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“…In the foreseeable future, AR applications and games will push for multi-agent collaboration and this is where C-SLAM comes into play (Egodagamage and Tuceryan, 2017;Egodagamage and Tuceryan, 2018). To that end, (Morrison et al, 2016) proposes a centralized approach in which virtual elements are shared by all agents, and (Sartipi et al, 2019) introduces a decentralized AR technique with smartphones, making use of the visual and inertial sensors already present in those devices.…”

Section: Augmented Realitymentioning

confidence: 99%

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.

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“…However, MR-SLAM also comes with new challenges stemming from i) the network and time synchronization constraints; ii) the limited computational resources the robots must rely on to process their own data additionally to multi-robot interactions; and iii) heterogeneous data fusion. [12] and VI-SLAM [13], [14] methods. Decentralization is also more conducive to the distribution of tasks and data to compensate for the lack of a central server as agents should process the data received from all the other agents.…”

Section: Introductionmentioning

confidence: 99%

AirMuseum: a heterogeneous multi-robot dataset for stereo-visual and inertial Simultaneous Localization And Mapping

Dubois

Eudes

Frémont

2020

2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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This paper introduces a new dataset dedicated to multi-robot stereo-visual and inertial Simultaneous Localization And Mapping (SLAM). This dataset consists in five indoor multi-robot scenarios acquired with ground and aerial robots in a former Air Museum at ONERA Meudon, France. Those scenarios were designed to exhibit some specific opportunities and challenges associated to collaborative SLAM. Each scenario includes synchronized sequences between multiple robots with stereo images and inertial measurements. They also exhibit explicit direct interactions between robots through the detection of mounted AprilTag markers [1]. Ground-truth trajectories for each robot were computed using Structure-from-Motion algorithms and constrained with the detection of fixed AprilTag markers placed as beacons on the experimental area. Those scenarios have been benchmarked on state-of-the-art monocular, stereo and visual-inertial SLAM algorithms to provide a baseline of the single-robot performances to be enhanced in collaborative frameworks.

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Decentralized Visual-Inertial Localization and Mapping on Mobile Devices for Augmented Reality

Cited by 10 publications

References 32 publications

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

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

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

AirMuseum: a heterogeneous multi-robot dataset for stereo-visual and inertial Simultaneous Localization And Mapping

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