Unified Multi-Modal Data Aggregation for Complementary Sensor Networks Applied for Localization

Berndt, Maximilian; Krummacker, Dennis; Fischer, Christoph; Schotten, Hans D.

doi:10.1109/vtc2022-spring54318.2022.9860496

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…As a result, firefighting robots usually carry a large number of different types of sensors. The information acquired by multiple sensors may complement or contradict each other [12,13]. The information acquired by each sensor has to be processed separately, increasing the workload.…”

Section: Introductionmentioning

confidence: 99%

A task level fusion autonomous switching mechanism

Lv,

Wang,

Zhang

2023

PLoS ONE

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Positioning technology is an important component of environmental perception. It is also the basis for autonomous decision-making and motion control of firefighting robots. However, some issues such as positioning in indoor scenarios still remain inherent challenges. The positioning accuracy of the fire emergency reaction dispatching (FERD) system is far from adequate to support some applications for firefighting and rescue in indoor scenarios with multiple obstacles. To solve this problem, this paper proposes a fusion module based on the Blackboard architecture. This module aims to improve the positioning accuracy of a single sensor of the unmanned vehicles within the FERD system. To reduce the risk of autonomous decision-making of the unmanned vehicles, this module uses a comprehensive manner of multiple channels to complement or correct the positioning of the firefighting robots. Specifically, this module has been developed to fusion a variety of relevant processes for precise positioning. This process mainly includes six strategies. These strategies are the denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision. These strategies merge with the current scenarios-related parameter data, empirical data on sensor errors, and information to form a series of norms. This paper then proceeds to gain experience data with the confidence degree, error of different sensors, and timeliness of this module by training in an indoor scenario with multiple obstacles. This process is from data of multiple sensors (bottom-level) to control decisions knowledge-based (up-level). This process can obtain globally optimal positioning results. Finally, this paper evaluates the performance of this fusion module for the FERD system. The experimental results show that this fusion module can effectively improve positioning accuracy in an indoor scenario with multiple obstacles. Code is available at https://github.com/lvbingyu-zeze/gopath/tree/master.

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Section: Introductionmentioning

confidence: 99%

A task level fusion autonomous switching mechanism

Lv,

Wang,

Zhang

2023

PLoS ONE

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Analysis of 5G Channel Access for Collaboration with TSN Concluding at a 5G Scheduling Mechanism

Krummacker

Fischer

Schotten

2022

Network

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As 5G enters the application field of industrial communications, compatibility with technologies of wired deterministic communications such as Time-Sensitive Networking (TSN) needs to be considered during the standardization process. While consideration of underlying integration architectures and basic resource mapping are already part of the standard, necessary traffic forwarding schemes are currently planned to be deployed in additional interfaces located at the edge of a 5G System. This analysis highlights the extent to which internal 5G mechanisms can be used to execute the traffic forwarding of TSN streams according to the requirements of the TSN control plane. It concludes with the recognition that a static prioritization of logical channels is not appropriate for the treatment of TSN streams over the 5G air interface. A novel prioritization mechanism of logical data channels is derived, which enables the execution of TSN-compliant traffic shaping over 5G RAN. Subsequently, a proof of concept is implemented and simulated for evaluation.

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Unified Multi-Modal Data Aggregation for Complementary Sensor Networks Applied for Localization

Cited by 2 publications

References 16 publications

A task level fusion autonomous switching mechanism

A task level fusion autonomous switching mechanism

Analysis of 5G Channel Access for Collaboration with TSN Concluding at a 5G Scheduling Mechanism

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