The degree of accuracy by which a mobile robot can estimate the properties of its surrounding environment, and the ability to successfully navigate throughout the explored space are the main factors that may well determine its autonomy and efficiency with respect to the goals of the application. This paper focuses on the implementation of a SLAM framework comprising a planner, a percept and a displacement/angular error estimator using a regular occupancy grid spatial memory representation. The overall process is arbitrated by a finite state machine (FSM) invoking the components of the framework according to the deliberate paradigm, SENSE-PLAN-ACT as described by R. Murphy [1], with the occasional exception of the interstitial invocation of a correction process, based on the estimated angular and displacement error during the execution of the planned tasks. The FSM uses a global task queue in order to execute the tasks that the planner produced during the previous call.
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