Titlebook: Edge Assisted Mobile Visual SLAM; Jingao Xu,Zheng Yang,Hao Cao Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive

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EdgeSLAM 1.0: Architectural Innovations in Mobile Visual SLAMomputing. Location services, critical for a myriad of applications, have garnered extensive attention from both the academic sphere and the industry sector. The rise of visual SLAM in domains such as robotics and autonomous vehicles marks a significant advancement. Nonetheless, its deployment on dev

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EdgeSLAM 2.0: Enhancing Scalability in Multi-Agent Systemsecifically tailored for burgeoning applications like search-and-rescue operations, inventory management automation, and industrial inspections. Central to SwarmMap’s architecture are three innovative modules: a server–client synchronization process based on change logs, a scheduler attuned to task p

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Large-Scale Crowdsourced Mapping with EdgeSLAMn to simplify setup processes and visual techniques to forgo infrastructure dependency. Yet, challenges persist: P2P navigation often grapples with path scarcity, and visual techniques on smartphones face issues such as scale uncertainty, directionality limitations, and high computational demands. A

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Research Summary and Open Issuesn academia, visual SLAM systems have gained more attention from the industry, making their way toward robots, drones, and our daily lives. However, several challenges still need to be overcome before edge-assisted mobile visual SLAM is put into ubiquitous use.

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-assisted visual SLAM.Presents in-depth case studies on indo.In an age where real-time processing and interaction with the physical world through digital lenses are paramount, visual SLAM technology has become the backbone of mobile AR/VR applications, robotics, and autonomous systems. However, the