CoSLAM: Collaborative Visual SLAM in Dynamic Environments

Zou, Danping; Tan, Ping

doi:10.1109/tpami.2012.104

Cited by 325 publications

(172 citation statements)

References 34 publications

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“…By matching 3-D points and lines, robots can estimate their relative poses and fuse their local maps to maintain geometric consistency and achieve effective cooperation in large-scale environments [189]. Robots may also maintain the position uncertainty of each point in the map for handling of dynamic objects [190]. Early work on visionbased, collaborative SLAM for aerial robots was introduced in [184], in which a centralized ground station was used to collect data from multiple aerial robots.…”

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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Section: Cooperative Aerial Mappingmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…Zou and Tan in [22] allowed cameras to move independently in a dynamic environment. However, all of their cameras were initialized from the same scene and connected to the same computer.…”

Section: Distributed Slammentioning

confidence: 99%

Distributed Monocular SLAM for Indoor Map Building

Egodagamage

Tüceryan

2017

Journal of Sensors

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Utilization and generation of indoor maps are critical elements in accurate indoor tracking. Simultaneous Localization and Mapping (SLAM) is one of the main techniques for such map generation. In SLAM an agent generates a map of an unknown environment while estimating its location in it. Ubiquitous cameras lead to monocular visual SLAM, where a camera is the only sensing device for the SLAM process. In modern applications, multiple mobile agents may be involved in the generation of such maps, thus requiring a distributed computational framework. Each agent can generate its own local map, which can then be combined into a map covering a larger area. By doing so, they can cover a given environment faster than a single agent. Furthermore, they can interact with each other in the same environment, making this framework more practical, especially for collaborative applications such as augmented reality. One of the main challenges of distributed SLAM is identifying overlapping maps, especially when relative starting positions of agents are unknown. In this paper, we are proposing a system having multiple monocular agents, with unknown relative starting positions, which generates a semidense global map of the environment.

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“…However, loop closure and map merging were only possible when another robot was recognized visually. In [16] the authors present a collaborative visual SLAM system for dynamic environments that is capable of tracking camera pose over time and deciding if some of the cameras observe the same scene; information is combined into groups that run the tracking together. More recently, several visualinertial odometry systems [17], including Google's Project Tango [18] that runs on a custom cellphone with specialized hardware, has shown superior accuracy and consistency over the other approaches.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Collaborative 3D Mapping in Real-Time With Low-Cost Robots

Mohanarajah

Usenko

Singh³

et al. 2015

IEEE Trans. Automat. Sci. Eng.

130

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Abstract-This paper presents an architecture, protocol, and parallel algorithms for collaborative 3D mapping in the cloud with low-cost robots. The robots run a dense visual odometry algorithm on a smartphone-class processor. Key-frames from the visual odometry are sent to the cloud for parallel optimization and merging with maps produced by other robots. After optimization the cloud pushes the updated poses of the local key-frames back to the robots. All processes are managed by Rapyuta, a cloud robotics framework that runs in a commercial data center. The paper includes qualitative visualization of collaboratively built maps, as well as quantitative evaluation of localization accuracy, bandwidth usage, processing speeds, and map storage.Note to Practitioners-This paper presents an architecture for cloud-based collaborative 3D mapping with low-cost robots. The low-cost robots used in this work consist mainly of a mobile base, a smart phone class processor, an RGB-D sensor and a wireless interface. Each robot runs its own visual odometry algorithm, which estimates the pose of the robot using the color and the depth frames (images) from the RGB-D sensor. The dense visual odometry algorithm presented herein uses no image features and requires no specialized hardware. In addition to pose estimation, the visual odometry algorithm also produces key-frames, which is a subset of frames that in a way summarizes the motion of the robot. These key-frames are sent to the cloud for further optimization and merging with the key-frames produced by other robots. By sending only the key-frames (instead of all the frames produced by the sensor), bandwidth requirements are significantly reduced. Each robot is connected to the cloud infrastructure using a WebSocket-based bidirectional full duplex communication channel. The cloud infrastructure is provided using Rapyuta, a Platform-as-a-Service framework for building scalable cloud robotics applications. The key-frame pose optimization and the merging processes are parallelized in order to make them scalable. The updated key-frame poses are eventually sent back to the robot to improve its localization accuracy. In addition to describing the architecture and the design choices, the paper provides qualitative and quantitative evaluations of the integrated system.

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CoSLAM: Collaborative Visual SLAM in Dynamic Environments

Cited by 325 publications

References 34 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Distributed Monocular SLAM for Indoor Map Building

Cloud-Based Collaborative 3D Mapping in Real-Time With Low-Cost Robots

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