Computation Sharing in Distributed Robotic Systems: A Case Study on SLAM


Aiming at increasing team potency, mobile robots could act as a node of a Robotic Cluster to help their teammates in computationally demanding tasks. Having this in mind, we propose two distributed architectures for the Simultaneous Localization And Mapping (SLAM) downside, our main case study. The analysis focuses particularly on the efficiency gain that can be obtained. It's shown that the proposed architectures enable us to lift the workload up to values that would not be attainable in a single robot answer, therefore gaining in localization precision and map accuracy. Furthermore, we assess the impact of network bandwidth. All the results are extracted from frequently used SLAM datasets out there within the robotics community and a true world testbed is described to point out the potential of using the proposed philosophy.

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