Enhancing Robustness and Accuracy in Edge-Assisted Visual SLAM Implementation

With the increasing demand for spatial positioning on modern mobile devices, Simultaneous Localization and Mapping (SLAM), particularly camera-based Visual SLAM, has become essential for real-time perception and positioning by processing continuous image data. However,these algorithms often entail h...

Full description

Saved in:
Bibliographic Details
Published inInternational Journal of Networking and Computing Vol. 15; no. 2; pp. 85 - 101
Main Authors Xia, Chenzhang, Wang, Yuan, Inoue, Koji
Format Journal Article
LanguageEnglish
Japanese
Published IJNC Editorial Committee 2025
Subjects
edge assisted slam
edge computing
multithreaded system
slam relocalization
thread refactoring
visual slam
Online AccessGet full text
ISSN2185-2839
2185-2847
DOI10.15803/ijnc.15.2_85

Cover

More Information
Summary:With the increasing demand for spatial positioning on modern mobile devices, Simultaneous Localization and Mapping (SLAM), particularly camera-based Visual SLAM, has become essential for real-time perception and positioning by processing continuous image data. However,these algorithms often entail high memory and computational requirements, making it challenging to deploy them on mobile devices and run for extended periods. To address this issue, the edge-assisted SLAM architecture, which offloads computationally intensive tasks toedge servers, has been proposed. Despite its potential, existing solutions in this domain suffer from significant limitations in data synchronization and recovery capability, compromising both the robustness and accuracy of the system. In response to the identified limitations, we analyze the impact of the current data synchronization and relocalization recovery processes on system performance, and introduce a novel multithreaded tracking approach integrated with an efficient relocalization mechanism. We validated our approach in standard datasets, including the robustness of the system, tracking recovery capability, and localization accuracy. Experimentalresults demonstrate that our solution reduces tracking interruptions by up to 94.2%, significantly improves coverage, a vital robustness metric of the SLAM system, by up to 30.1%, and shortens relocalization recovery time by up to 35.2%. Furthermore, our approach improvesthe localization accuracy by 43.7% in translation scenarios and 36.8% in rotation scenarios.
ISSN:2185-2839
2185-2847
DOI:10.15803/ijnc.15.2_85