TY - GEN
T1 - Edge-SLAM
T2 - 18th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2020
AU - Ali, Ali J.Ben
AU - Hashemifar, Zakieh Sadat
AU - Dantu, Karthik
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/6/15
Y1 - 2020/6/15
N2 - Localization in urban environments is becoming increasingly important and used in tools such as ARCore [11], ARKit [27] and others. One popular mechanism to achieve accurate indoor localization as well as a map of the space is using Visual Simultaneous Localization and Mapping (Visual-SLAM). However, Visual-SLAM is known to be resource-intensive in memory and processing time. Further, some of the operations grow in complexity over time, making it challenging to run on mobile devices continuously. Edge computing provides additional compute and memory resources to mobile devices to allow offloading of some tasks without the large latencies seen when offloading to the cloud. In this paper, we present Edge-SLAM, a system that uses edge computing resources to offload parts of Visual-SLAM. We use ORB-SLAM2 as a prototypical Visual-SLAM system and modify it to a split architecture between the edge and the mobile device. We keep the tracking computation on the mobile device and move the rest of the computation, i.e., local mapping and loop closure, to the edge. We describe the design choices in this effort and implement them in our prototype. Our results show that our split architecture can allow the functioning of the Visual-SLAM system long-term with limited resources without affecting the accuracy of operation. It also keeps the computation and memory cost on the mobile device constant which would allow for deployment of other end applications that use Visual-SLAM.
AB - Localization in urban environments is becoming increasingly important and used in tools such as ARCore [11], ARKit [27] and others. One popular mechanism to achieve accurate indoor localization as well as a map of the space is using Visual Simultaneous Localization and Mapping (Visual-SLAM). However, Visual-SLAM is known to be resource-intensive in memory and processing time. Further, some of the operations grow in complexity over time, making it challenging to run on mobile devices continuously. Edge computing provides additional compute and memory resources to mobile devices to allow offloading of some tasks without the large latencies seen when offloading to the cloud. In this paper, we present Edge-SLAM, a system that uses edge computing resources to offload parts of Visual-SLAM. We use ORB-SLAM2 as a prototypical Visual-SLAM system and modify it to a split architecture between the edge and the mobile device. We keep the tracking computation on the mobile device and move the rest of the computation, i.e., local mapping and loop closure, to the edge. We describe the design choices in this effort and implement them in our prototype. Our results show that our split architecture can allow the functioning of the Visual-SLAM system long-term with limited resources without affecting the accuracy of operation. It also keeps the computation and memory cost on the mobile device constant which would allow for deployment of other end applications that use Visual-SLAM.
KW - Edge computing
KW - Localization
KW - Mapping
KW - Mobile Systems
KW - Split architecture
KW - Visual simultaneous localization and mapping
UR - https://www.scopus.com/pages/publications/85088125321
U2 - 10.1145/3386901.3389033
DO - 10.1145/3386901.3389033
M3 - Conference contribution
AN - SCOPUS:85088125321
T3 - MobiSys 2020 - Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services
SP - 325
EP - 337
BT - MobiSys 2020 - Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services
PB - Association for Computing Machinery, Inc
Y2 - 15 June 2020 through 19 June 2020
ER -