On the Performance Analysis of Active Visual 3D Reconstruction in Multi-agent Networks

An, Qier; Shen, Yuan

doi:10.1109/wcsp.2019.8928085

Cited by 8 publications

(10 citation statements)

References 13 publications

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“…The papers that were published prior to 2015 were dropped. The resulting list contained 17 papers [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], which were filtered by excluding papers that did not comply to at least one of the following requirements: Due to the formulation of the problem task, reviewed papers should be characterized by a set of unified criteria that describe how the main challenges are handled. These criteria can be structured in a form of five questions for each paper:…”

Section: State Of the Artmentioning

confidence: 99%

“…Systems of a homogeneous architecture (i.e., all drones have the same role) with a centralized control are the most common among all reviewed articles [3,9,10,[13][14][15]. A big subset of these solutions [9,10,13,14] organize control by a similar scheme-all drones capture images and/or videos using a predefined set or independent trajectories [9,14], set of predefined waypoints [10] or moving in a stable formation by a single predefined trajectory [13].…”

Section: What Multiagent Approach Is Used?mentioning

confidence: 99%

“…Ref. [3] builds a statistical model for multi-camera visual 3D reconstruction problem and then proposes a Next Best View (NBV) selection scheme to determine the best camera configuration in active visual 3D reconstruction.…”

Section: What Multiagent Approach Is Used?mentioning

confidence: 99%

“…All reviewed solutions can be divided to three groups by the localization approach-SLAM-based [6,7,11,15,18,19], machine learning-based [5,8,12] and solutions that rely on any drone built-in technologies, which rely on a visual markup or do not specify any particular approach [3,4,9,10,13,14,16,17].…”

Section: What Localization Approach Is Used?mentioning

confidence: 99%

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Multi-Drone 3D Building Reconstruction Method

2021

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In the recent decade, the rapid development of drone technologies has made many spatial problems easier to solve, including the problem of 3D reconstruction of large objects. A review of existing solutions has shown that most of the works lack the autonomy of drones because of nonscalable mapping techniques. This paper presents a method for centralized multi-drone 3D reconstruction, which allows performing a data capturing process autonomously and requires drones equipped only with an RGB camera. The essence of the method is a multiagent approach—the control center performs the workload distribution evenly and independently for all drones, allowing simultaneous flights without a high risk of collision. The center continuously receives RGB data from drones and performs each drone localization (using visual odometry estimations) and rough online mapping of the environment (using image descriptors for estimating the distance to the building). The method relies on a set of several user-defined parameters, which allows the tuning of the method for different task-specific requirements such as the number of drones, 3D model detalization, data capturing time, and energy consumption. By numerical experiments, it is shown that method parameters can be estimated by performing a set of computations requiring characteristics of drones and the building that are simple to obtain. Method performance was evaluated by an experiment with virtual building and emulated drone sensors. Experimental evaluation showed that the precision of the chosen algorithms for online localization and mapping is enough to perform simultaneous flights and the amount of captured RGB data is enough for further reconstruction.

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Section: State Of the Artmentioning

confidence: 99%

Section: What Multiagent Approach Is Used?mentioning

confidence: 99%

Section: What Multiagent Approach Is Used?mentioning

confidence: 99%

Section: What Localization Approach Is Used?mentioning

confidence: 99%

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Multi-Drone 3D Building Reconstruction Method

2021

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“…When a 3D point is observed in multiple images with different camera positions (indexed by i ∈ N ), the total hybrid FIM J is the sum of Ji of each image derived from (3). And the reconstruction error is evaluated based on the FIM-metric [19,21] as…”

Section: System Modelmentioning

confidence: 99%

On The Camera Position Dithering In Visual 3d Reconstruction

Shen

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The performance of visual 3D reconstruction is affected by camera position dithering, imaging noise and other environmental factors. In this paper, we adopt statistical analysis for camera dithering in visual 3D reconstruction and demonstrate the improvement of reconstruction accuracy brought by camera dithering under some conditions. In particular, we first investigate the influence of camera dithering and imaging noise on the reconstruction accuracy based on hybrid Cramér-Rao Lower Bound (CRLB) analysis. Then we focus on the binocular reconstruction and give closed-form expressions and geometric interpretations to clarify that camera dithering helps reduce reconstruction error. As an extension, the impacts of disparity and quantization error in binocular reconstruction are also analyzed. Numerical results validate our conclusions.

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Cooperative Dynamic Coverage Control in Wireless Camera Sensor Networks with Anisotropic Perception

Wang

et al. 2021

2021 IEEE Global Communications Conference (GLOBECOM)

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On the Performance Analysis of Active Visual 3D Reconstruction in Multi-agent Networks

Cited by 8 publications

References 13 publications

Multi-Drone 3D Building Reconstruction Method

Multi-Drone 3D Building Reconstruction Method

On The Camera Position Dithering In Visual 3d Reconstruction

Cooperative Dynamic Coverage Control in Wireless Camera Sensor Networks with Anisotropic Perception

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