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Edge Computing Assisted Autonomous Flight for UAV: Synergies between Vision and Communications
Open accessAbstract
Autonomous flight for UAVs relies on visual information for avoiding obstacles and ensuring safe collision-free flight. In addition to visual clues, safe UAVs often need connectivity with the ground station. In this article, we study the synergies between vision and communications for edge-computing-enabled UAV flight. By proposing a framework of edge computing assisted autonomous flight (ECAAF), we illustrate that vision and communications can interact with and assist each other with the aid of edge computing and offloading, and further speed up UAV mission completion. ECAAF consists of three functionalities that are discussed in detail: edge computing for 3D map acquisition, radio map construction from the 3D map, and online trajectory planning. During ECAAF, the interactions of communication capacity, video offloading, 3D map quality, and channel state of the trajectory form a positive feedback loop. Simulation results verify that the proposed method can improve mission performance by enhancing connectivity. Finally, we conclude with some future research directions.
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SLAM has become a critical enabling technology for many emerging human-centered applications such as AR/VR, HD maps, and autonomous navigation.
“Recently, SLAM has become a critical enabling technology for many emerging human-centered applications, such as AR/VR [45], HD map [6], [46], and autonomous navigation [47].”
From Orchestrating Joint Offloading and Scheduling for Low-Latency Edge SLAM (2025) · cites this paper as [47]
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Research works printed but not linked: 15
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Orchestrating Joint Offloading and Scheduling for Low-Latency Edge SLAM
Zhang, Y., Mao, Y., Wang, H., Yu, Z., Guo, S., Zhang, J., Wang, L., & Guo, B. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3547256 · Cited by 1
“In terms of mobile robotics systems with limited computation resources, edge-assisted SLAM has drawn substantial attention since it has a great potential to reduce the computation burden on the client side, such as autonomous vehicles [48], UVAs [49].”
Metadata
- Authors
- Chen, Q., Zhu, H., Yang, L., Chen, X., Pollin, S., & Vinogradov, E.
- Affiliations
- KU Leuven (BE)
- Delft University of Technology (NL)
- National University of Defense Technology (CN)
- Venue
- IEEE Communications Magazine
- Published
- 2021-01
- Volume
- 59
- Issue
- 1
- Pages
- 28-33
- ISSN
- 0163-6804 (ISSN-L)
- 1558-1896
- OpenAlex
- W3130574574
- Cited by
- 62
- References
- 0
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 9 · 2023: 11 · 2022: 15 · 2021: 4
- FWCI
- 5.03
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Communications Magazine (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Research Repository (Delft University of Technology) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Natural Science Foundation of China (11725211)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Control and Systems Engineering › Robotic Path Planning Algorithms
- Keywords
- edge computing, unmanned aerial vehicles, autonomous flight, radio map construction, online trajectory planning, obstacle avoidance, communication skills, channel state
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