In this paper, an extremely efficient architecture for
the Simultaneous Localization and Mapping (SLAM) problem
is proposed. This architecture depends on distributing of heavy
computational tasks and large data sets among remote servers
and frees the robots from any computational loads. Thus, the
most widely used FastSLAM2.0 approach is parallelized as
Map/Reduce tasks via the Hadoop framework. The experiments
show the real-time performance for a single robot navigation in
two scenarios: the traditional method as sequential algorithm and
the presented scheme which is used to estimate fast a parallel
algorithm. Also, the contribution of this paper is segmentation
of the FastSLAM 2.0 algorithm to execute concurrently the
localization and the mapping tasks on the cloud for overcoming
strong real-time constraints of the localization task.