This article describes how an animat endowed with the MonaLysa control architecture can build a cognitive map that merges into a hierarchical framework not only topological links between landmarks but also higher-level structures, control information, and metric distances and orientations. The article also describes how the animat can use such a map to locate itself, even if it is endowed with noisy dead-reckoning capacities. MonaLysa's mapping and self-positioning capacities are illustrated by results obtained in three different environments and four noise-level conditions. These capacities appear to be gracefully degraded when the environment grows more challenging and when the noise level increases. In the discussion, the current approach is compared to others with similar objectives, and directions for future work are outlined.
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