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 of edgeSLAM, an edge-assisted real-time semantic visual SLAM service running on mobile devices. edgeSLAM leverages 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 of edgeSLAM include an efficient computation offloading strategy, an opportunistic data sharing method, an adaptive task scheduling algorithm, and a multi-user support mechanism. We fully implement edgeSLAM and plan to open-source it. Extensive experiments are conducted under 3 datasets. The results show that edgeSLAM can 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 of edgeSLAM through 2 case studies of pedestrian localization and robot navigation. To the best of our knowledge, edgeSLAM is the first edge-assisted real-time semantic visual SLAM for mobile devices.