UAVs for smart cities: Opportunities and challenges

Mohammed, Farhan; Idries, Ahmed; Mohamed, Nader; Al-Jaroodi, Jameela; Jawhar, Imad

doi:10.1109/icuas.2014.6842265

Cited by 265 publications

(125 citation statements)

References 5 publications

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“…20-30 small UAVs transmitting high-resolution videos to the GS). The list of such civilian and military applications for UAVs keeps growing [2], [5]- [7]. Therefore, a new breakthrough technology is required in order to support the multitude of applications of UAVs that need high throughput, low power consumption, and low latency.…”

Section: Introductionmentioning

confidence: 99%

Massive MIMO as enabler for communications with drone swarms

Chandhar

Danev

Larsson

2016

2016 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-Massive multiple-input multiple-output (MIMO) is an emerging technology for mobile communications, where a large number of antennas are employed at the base station to simultaneously serve multiple single-antenna terminals with very high capacity. In this paper, we study the potentials and challenges of utilizing massive MIMO for unmanned aerial vehicles (UAVs) communication. We consider a scenario where multiple single-antenna UAVs simultaneously communicate with a ground station (GS) equipped with a large number of antennas. Specifically, we discuss the achievable uplink (UAV to GS) capacity performance in the case of line-of-sight (LoS) conditions. We also study the type of antenna polarization that should be used in order to maintain a reliable communication link between the GS and the UAVs. The results obtained using a realistic geometric model show that massive MIMO is a potential enabler for high-capacity UAV networks.

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Section: Introductionmentioning

confidence: 99%

Massive MIMO as enabler for communications with drone swarms

Chandhar

Danev

Larsson

2016

2016 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…where (1), (2), and (3) are the negative log-likelihood of the IMU measurements (∆R ij , ∆ṽ ij , ∆p ij ), vision measurements (u l ik , u r ik ), 2 and the priors, generally referred to as factors, since the problem can be understood as the result of performing inference over a factor graph. In eqs.…”

Section: Vio: Algorithmic Overviewmentioning

confidence: 99%

“…The marginalization process requires linearizing the problem, similarly to what is done in GN, and performing a Cholesky factorization. 2 The observation of landmark l k ∈ R 3 in keyframe i produces a stereo measurement (u l ik , u r ik ), which is the pair of pixels corresponding to the projection of landmark l k onto the left and right camera frame at time i. …”

Section: Vio: Algorithmic Overviewmentioning

confidence: 99%

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Visual-Inertial Odometry on Chip: An Algorithm-and-Hardware Co-design Approach

Zhang

Suleiman

Carlone

et al. 2017

Robotics: Science and Systems XIII

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Abstract-Autonomous navigation of miniaturized robots (e.g., nano/pico aerial vehicles) is currently a grand challenge for robotics research, due to the need for processing a large amount of sensor data (e.g., camera frames) with limited on-board computational resources. In this paper we focus on the design of a visual-inertial odometry (VIO) system in which the robot estimates its ego-motion (and a landmark-based map) from onboard camera and IMU data. We argue that scaling down VIO to miniaturized platforms (without sacrificing performance) requires a paradigm shift in the design of perception algorithms, and we advocate a co-design approach in which algorithmic and hardware design choices are tightly coupled. Our contribution is four-fold. First, we discuss the VIO co-design problem, in which one tries to attain a desired resource-performance trade-off, by making suitable design choices (in terms of hardware, algorithms, implementation, and parameters). Second, we characterize the design space, by discussing how a relevant set of design choices affects the resource-performance trade-off in VIO. Third, we provide a systematic experiment-driven way to explore the design space, towards a design that meets the desired trade-off. Fourth, we demonstrate the result of the co-design process by providing a VIO implementation on specialized hardware and showing that such implementation has the same accuracy and speed of a desktop implementation, while requiring a fraction of the power.

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“…They provide essential information for smart city monitoring and management (Gruen, 2013;Mohammed et al, 2014). As small flying vehicles they can be used for fast change detection and disaster response (Qin, 2014).…”

Section: Introductionmentioning

confidence: 99%

Quality Analysis on 3d Buidling Models Reconstructed From Uav Imagery

Jarzabek-Rychard¹,

Karpina²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Recent developments in UAV technology and structure from motion techniques have effected that UAVs are becoming standard platforms for 3D data collection. Because of their flexibility and ability to reach inaccessible urban parts, drones appear as optimal solution for urban applications. Building reconstruction from the data collected with UAV has the important potential to reduce labour cost for fast update of already reconstructed 3D cities. However, especially for updating of existing scenes derived from different sensors (e.g. airborne laser scanning), a proper quality assessment is necessary. The objective of this paper is thus to evaluate the potential of UAV imagery as an information source for automatic 3D building modeling at LOD2. The investigation process is conducted threefold: (1) comparing generated SfM point cloud to ALS data; (2) computing internal consistency measures of the reconstruction process; (3) analysing the deviation of Check Points identified on building roofs and measured with a tacheometer. In order to gain deep insight in the modeling performance, various quality indicators are computed and analysed. The assessment performed according to the ground truth shows that the building models acquired with UAV-photogrammetry have the accuracy of less than 18 cm for the plannimetric position and about 15 cm for the height component.

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UAVs for smart cities: Opportunities and challenges

Cited by 265 publications

References 5 publications

Massive MIMO as enabler for communications with drone swarms

Massive MIMO as enabler for communications with drone swarms

Visual-Inertial Odometry on Chip: An Algorithm-and-Hardware Co-design Approach

Quality Analysis on 3d Buidling Models Reconstructed From Uav Imagery

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