C2TAM: A Cloud framework for cooperative tracking and mapping

Riazuelo, Luis; Civera, Javier; Montiel, José María

doi:10.1016/j.robot.2013.11.007

Cited by 228 publications

(121 citation statements)

References 35 publications

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“…One well-known example can be found in [214]. In this article, the authors expose a cloud framework, C 2 TAM, which shares the workload between a cloud server and the robot.…”

Section: A Centralized Slammentioning

confidence: 99%

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

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

703

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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“…One well-known example can be found in [214]. In this article, the authors expose a cloud framework, C 2 TAM, which shares the workload between a cloud server and the robot.…”

Section: A Centralized Slammentioning

confidence: 99%

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

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

703

366

View full text Add to dashboard Cite

show abstract

“…RGB-D cameras that provide both depth and color information for each image are often used for cooperative visual dense mapping. Due to the high computational load required to process dense maps in real time, robots with limited computational power may choose to send local maps to a cloud server to perform map merging and batch optimization [198], [199]. Recent work demonstrated real-time pose estimation for autonomous flight and cooperative dense mapping using onboard computation with two quadrotors equipped with RGB-D cameras [200], and with a heterogeneous team of a quadrotor and a ground robot [201].…”

Section: Cooperative Aerial Mappingmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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“…Cloud robotics is an emerging field combining the research areas of cloud computing and robotics to provide services to robots and facilitate robot interaction [21]- [23]. This includes utilising cloud services for robotics [24]- [26] and robots themselves providing services [27].…”

Section: Background and Motivationmentioning

confidence: 99%

Massive-Scale Automation in Cyber-Physical Systems: Vision & Challenges

McKee

Clement

Almutairi

et al. 2017

2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS)

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Abstract-The next era of computing is the evolution of the Internet of Things (IoT) and Smart Cities with development of the Internet of Simulation (IoS). The existing technologies of Cloud, Edge, and Fog computing as well as HPC being applied to the domains of Big Data and deep learning are not adequate to handle the scale and complexity of the systems required to facilitate a fully integrated and automated smart city. This integration of existing systems will create an explosion of data streams at a scale not yet experienced. The additional data can be combined with simulations as services (SIMaaS) to provide a shared model of reality across all integrated systems, things, devices, and individuals within the city. There are also numerous challenges in managing the security and safety of the integrated systems. This paper presents an overview of the existing stateof-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. Additionally the key challenges relating to Big Data, a model of reality, augmentation of systems, computation, and security are examined.

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C2TAM: A Cloud framework for cooperative tracking and mapping

Cited by 228 publications

References 35 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

A Survey on Aerial Swarm Robotics

Massive-Scale Automation in Cyber-Physical Systems: Vision & Challenges

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