A Progressive Transmission Technique for the Streaming of Point Cloud Data Using the Kinect

Moreno, Carlos; Li, Ming

doi:10.1109/iccnc.2018.8390310

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…The first one was the DASH-PC [23], which sent different parts of a point cloud depending on the user's current view and the available network data rate. The second one was adapting voxel length according to the available network data rate [24], [25].…”

Section: Communication Protocols and Point Cloud Streamingmentioning

confidence: 99%

Mixed Reality Human–Robot Interface With Adaptive Communications Congestion Control for the Teleoperation of Mobile Redundant Manipulators in Hazardous Environments

et al. 2022

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Robotic interventions with redundant mobile manipulators pose a challenge for telerobotics in hazardous environments, such as underwater, underground, nuclear facilities, particle accelerators, aerial or space. Communication issues can lead to critical consequences, such as imprecise manipulation resulting in collisions, breakdowns and mission failures. The research presented in this paper was driven by the needs of a real robotic intervention scenario in the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN). The goal of the work was to develop a framework for network optimisation in order to help facilitate Mixed Reality techniques such as 3D collision detection and avoidance, trajectories planning, real-time control, and automatized target approach. The teleoperator was provided with immersive interactions while preserving precise positioning of the robot. These techniques had to be adapted to delays, bandwidth limitation and their volatility in the 4G shared network of the real underground particle accelerator environment. The novel application-layer congestion control with automatic settings was applied for video and point cloud feedback. Twelve automatic setting modes were proposed with algorithms based on the camera frame rate, resolution, point cloud subsampling, network round-trip time and throughput to bandwidth ratio. Each mode was thoroughly characterized to present its specific use-case scenarios and the improvements it brings to the adaptive camera feedback control in teleoperation. Finally, the framework was presented according to which designers can optimize their Human-Robot Interfaces and sensor feedback depending on the network characteristics and task.

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Section: Communication Protocols and Point Cloud Streamingmentioning

confidence: 99%

Mixed Reality Human–Robot Interface With Adaptive Communications Congestion Control for the Teleoperation of Mobile Redundant Manipulators in Hazardous Environments

et al. 2022

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“…The authors of [4] and [5] try to live stream point clouds captured by a Microsoft Kinect in real-time at 30 frames per second (FPS). In [4], they adapt the voxel length of the doublebuffered octree [3] to the measured available network data rate in an interval of 15 frames and achieved an average of 5.86 FPS.…”

Section: Related Workmentioning

confidence: 99%

“…In [4], they adapt the voxel length of the doublebuffered octree [3] to the measured available network data rate in an interval of 15 frames and achieved an average of 5.86 FPS. In [5], they propose a new distributed octree data structure to increase the performance. An octree is precomputed for a given point cloud.…”

Section: Related Workmentioning

confidence: 99%

On the Potential of Rate Adaptive Point Cloud Streaming on the Point Level

Laniewski

Aschenbruck

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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In many robotic applications as well as in the area of immersive multimedia, large point clouds need to be transmitted over a network preferably in real-time. Typically, the point cloud is first compressed to a significantly smaller size, before it is transmitted as a simple file transfer. A drawback of this method is that it requires a point cloud to be fully available for the compression process to begin, inducing additional delay. In this paper, we explore the potential of streaming incoming points directly from an ongoing laser scan. Therefore, we propose a compression algorithm that operates on an ongoing point stream. Furthermore, we propose and compare different rate adaptation methods that dynamically adapt the compression rate to the currently available network data rate. Our evaluations show promising results in terms of rate fluctuations around the available network data rate and the achieved quality of our solution.

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“…Semi-Supervised GAN Structure. Figure4shows the network structure.Due to the change of the discriminator structure[27], the loss function of the semi-supervised generative adversarial network (SGAN) has also changed. The loss function of generator G is…”

mentioning

confidence: 99%

Optimized Fault Diagnosis Algorithm under GAN and CNN Hybrid Model

Zhu

2022

Wireless Communications and Mobile Computing

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The network fault diagnosis algorithm is one of the most important algorithms to solve network transmission problems. In traditional network fault diagnosis, it mainly analyzes and troubleshoots the fault manually by comparing the alarm information of the network performance index with the expert experience database. Diagnosis methods based on manpower analysis will take up much material resources and manpower and increase maintenance costs. Therefore, there is an urgent need for a more efficient and intelligent fault diagnosis technology. In addition, as the current network is becoming more and more complex, and based on this consideration, a semi-supervised fault diagnosis algorithm is studied in this paper. It is a combination of the GAN and CNN models. Meanwhile, the method of combining relief and mutual information is applied to reducing the dimensionality of network feature parameters, and the optimal feature combination is selected. The fluctuations in the convergence of the generated confrontation network model are stabilized. Moreover, the simulation software is used to build the heterogeneous wireless network scenario studied. Meanwhile, an improved fault diagnosis model is constructed to verify that in the case of both GAN and CNN models, the accuracy of fault diagnosis algorithm can reach 98.6%, which is significantly higher than other comparative analysis methods. It has contributed to ensuring the user’s service experience and reducing the cost of network maintenance and operation.

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A Progressive Transmission Technique for the Streaming of Point Cloud Data Using the Kinect

Cited by 4 publications

References 16 publications

Mixed Reality Human–Robot Interface With Adaptive Communications Congestion Control for the Teleoperation of Mobile Redundant Manipulators in Hazardous Environments

Mixed Reality Human–Robot Interface With Adaptive Communications Congestion Control for the Teleoperation of Mobile Redundant Manipulators in Hazardous Environments

On the Potential of Rate Adaptive Point Cloud Streaming on the Point Level

Optimized Fault Diagnosis Algorithm under GAN and CNN Hybrid Model

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