Evaluating the Advantages of Remote SLAM on an Edge Cloud
Peter Sossalla, Justus Rischke, Johannes Hofer, Frank H. P. Fitzek
Abstract
The Simultaneous Localization and Mapping (SLAM) method is becoming more and more established for the localization of mobile robots in indoor environments. Due to the high complexity of SLAM, high computing resources are necessary, which leads to a shorter runtime. By using edge computing and higher bandwidths of new wireless technologies, the computing can be outsourced. In this work, the SLAM process is offloaded from a mobile robot to an edge cloud and the impact of more computing power is investigated. We show that outsourcing has performance advantages in terms of the update rate of the map generation as well as the localization.
Topics & Concepts
Cloud computingComputer scienceOutsourcingEdge computingSimultaneous localization and mappingEnhanced Data Rates for GSM EvolutionMobile robotWirelessRobotProcess (computing)Mobile edge computingArtificial intelligenceDistributed computingReal-time computingComputer visionTelecommunicationsOperating systemLawPolitical scienceRobotics and Automated SystemsRobotics and Sensor-Based LocalizationModular Robots and Swarm Intelligence