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Full Description
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 demanding computational load of visual SLAM often strains the limited resources of mobile devices, hindering performance and accuracy. This is exactly where edge computing comes to the forefront, offering a potent solution by performing data processing at the edge of the network, closer to the source of data.
This monograph is a pioneering exploration into how edge computing can elevate visual SLAM systems, overcoming the traditional challenges of computational intensity and resource constraints. Edge computing not only offloads heavy-duty processing from mobile devices to edge servers but also mitigates latency, enhances efficiency, and ensures robust, real-time performance. This monograph unveils the transformative potential of edge-assisted visual SLAM, presenting groundbreaking research and the latest advancements in task decoupling, collaborative mapping, and environmental interaction.
This monograph could serve as a scholarly resource for those within the fields of computer vision and mobile computing. It presents a detailed exploration of current research in edge-assisted visual SLAM and anticipates future developments, offering readers a comprehensive understanding of the field's trajectory and its implications for the next generation of mobile applications and autonomous systems.
Contents
Part I. The Background.- Chapter 1. Understanding Visual SLAM.- Chapter 2. Edge Computing in Mobile Visual Systems.- Part II. Edge-Assisted Visual SLAM: System Design Principle.- Chapter 3. EdgeSLAM 1.0: Architectural Innovations in Mobile Visual SLAM.- Chapter 4. EdgeSLAM 2.0: Enhancing Scalability in Multi-Agent Systems.- Part III. Edge-Assisted Visual SLAM: Innovations and Applications.- Chapter 5. Indoor Autonomous Navigation with EdgeSLAM.- Chapter 6. Large-Scale Crowdsourced Mapping with EdgeSLAM.- Chapter 7. Environment Understanding with EdgeSLAM.- Chapter 8. Multi-User AR with EdgeSLAM.- Part IV. Conclusion.- Chapter 9. Research Summary and Open Issues.
