Collaborative localization and formation flying using distributed stereo-vision

Piasco, Nathan; Marzat, Julien; Sanfourche, Martial

doi:10.1109/icra.2016.7487251

Cited by 33 publications

(24 citation statements)

References 17 publications

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“…The developed approach provides both sparse and dense mapping. In a similar manner, in [90] a fleet of aerial vehicles has been employed to form a collaborative stereo camera for localization tasks. The sensors used in the proposed scheme are a monocular camera, an IMU and a sonar for each agent.…”

Section: Visual Localization and Mappingmentioning

confidence: 99%

Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis

Nikolakopoulos

2017

J Intell Robot Syst

295

120

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During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges C. Kanellakis ( ) · G. Nikolakopoulos Luleå University of Technology, Luleå, Sweden e-mail: christoforos.kanellakis@ltu.se G. Nikolakopoulos e-mail: geonik@ltu.se addressed and future trends in autonomous agent development will be also provided.

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Section: Visual Localization and Mappingmentioning

confidence: 99%

Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis

Nikolakopoulos

2017

J Intell Robot Syst

295

120

View full text Add to dashboard Cite

show abstract

“…In [27], the authors present an approach where two UAVs equipped with monocular cameras and IMUs estimate relative poses along with absolute scale, thus acting as a collaborative stereo camera. Piasco et al [28] also present a distributed stereo system with multiple UAVs for collaborative localization with a focus on formation control. Forster et al [29] show a structure-from-motion based collaborative SLAM system, which contains a centralized ground station whose function is to merge maps created by various vehicles.…”

Section: Related Workmentioning

confidence: 99%

Vision Based Collaborative Path Planning for Micro Aerial Vehicles

Vemprala

Palli

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper, we present a framework for performing collaborative localization for groups of micro aerial vehicles (MAV) that use vision based sensing. The vehicles are each assumed to be equipped with a forward-facing monocular camera, and to be capable of communicating with each other. This collaborative localization approach is developed as a decentralized algorithm and built in a distributed fashion where individual and relative pose estimation techniques are combined for the group to localize against surrounding environments. The MAVs initially detect and match salient features between each other to create a sparse reconstruction of the observed environment, which acts as a global map. Once a map is available, each MAV performs feature detection and tracking with a robust outlier rejection process to estimate its own pose in 6 degrees of freedom. Occasionally, one or more MAVs can be tasked to compute poses for another MAV through relative measurements, which is achieved through multiple view geometry concepts. These relative measurements are then fused with individual measurements in a consistent fashion. We present the results of the algorithm on image data from MAV flights both in simulation and real life, and discuss the advantages of collaborative localization in improving pose estimation accuracy.

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“…In [13], the authors present an approach where two UAVs equipped with monocular cameras and IMUs estimate relative poses along with absolute scale, thus acting as a collaborative stereo camera. Piasco et al [14] also present a distributed stereo system with multiple UAVs for collaborative localization, with a focus on achieving formation control. Forster et al [15] show a collaborative SLAM system based on a structure from motion pipeline with a centralized ground station merging maps based on overlap: but the vehicles do not receive any additional information from the central server or other vehicles.…”

Section: Related Workmentioning

confidence: 99%

Monocular Vision based Collaborative Localization for Micro Aerial Vehicle Swarms

Vemprala

Saripalli

2018

2018 International Conference on Unmanned Aircraft Systems (ICUAS)

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In this paper, we present a vision based collaborative localization framework for groups of micro aerial vehicles (MAV). The vehicles are each assumed to be equipped with a forward-facing monocular camera, and to be capable of communicating with each other. This collaborative localization approach is built upon a distributed algorithm where individual and relative pose estimation techniques are combined for the group to localize against surrounding environments. The MAVs initially detect and match salient features between each other to create a sparse reconstruction of the observed environment, which acts as a global map. Once a map is available, each MAV performs feature detection and tracking with a robust outlier rejection process to estimate its own six degree-of-freedom pose. Occasionally, the MAVs can also fuse relative measurements with individual measurements through feature matching and multiple-view geometry based relative pose computation. We present the implementation of this algorithm for MAVs and environments simulated within Microsoft AirSim, and discuss the results and the advantages of collaborative localization.

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Collaborative localization and formation flying using distributed stereo-vision

Cited by 33 publications

References 17 publications

Survey on Computer Vision for UAVs: Current Developments and Trends

Survey on Computer Vision for UAVs: Current Developments and Trends

Vision Based Collaborative Path Planning for Micro Aerial Vehicles

Monocular Vision based Collaborative Localization for Micro Aerial Vehicle Swarms

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