Edge Assisted Mobile Semantic Visual SLAM

Localization and navigation play a key role in many location-based services and have attracted numerous research efforts. In recent years, visual SLAM has been prevailing for autonomous driving. However, the ever-growing computation resources demanded by SLAM impede its applications to resource-constrained mobile devices. In this paper, we present the design, implementation, and evaluation ofedgeSLAM, an edge-assisted real-time semantic visual SLAM service running on mobile devices.edgeSLAMleverages the state-of-the-art semantic segmentation algorithm to enhance localization and mapping accuracy, and speeds up the computation-intensive SLAM and semantic segmentation algorithms by computation offloading. The key innovations ofedgeSLAMinclude an efficient computation offloading strategy, an opportunistic data sharing method, an adaptive task scheduling algorithm, and a multi-user support mechanism. We fully implementedgeSLAMand plan to open-source it. Extensive experiments are conducted under 3 datasets. The results show thatedgeSLAMcan run on mobile devices at 35fps and achieve 5cm localization accuracy from real-world experiments, outperforming existing solutions by more than 15%. We also demonstrate the usability ofedgeSLAMthrough 2 case studies of pedestrian localization and robot navigation. To the best of our knowledge,edgeSLAMis the first edge-assisted real-time semantic visual SLAM for mobile devices.