Robotic SLAM: a Review from Fog Computing and Mobile Edge Computing Perspective

Offloading computationally expensive Simultaneous Localization and Mapping (SLAM) task for mobile robots have attracted significant attention during the last few years. Lack of powerful on-board compute capability in these energy constrained mobile robots and rapid advancement in compute cloud access technologies laid the foundation for development of several Cloud Robotics platforms that enabled parallel execution of computationally expensive robotic algorithms, especially involving multiple robots. In this work the Cloud Robotics concept is extended to include the current emphasis of computing at the network edge nodes along with the Cloud. The requirements and advantages of using edge nodes for computation offloading over remote cloud or local robot clusters are discussed with reference to the ETSI 'Mobile-Edge Computing' initiative and OpenFog Consortium's 'OpenFog Architecture'. A Particle Filter algorithm for SLAM is modified and implemented for offloading in a multi-tier edge+cloud setup. Additionally a model is proposed for offloading decision in such a setup with experiments and results demonstrating the efficacy of the proposed dynamic offloading scheme over static offloading strategies.