Autonomous robots are playing important roles in academic, technological, and scientific activities. Thus, their behavior is getting more complex, particularly, in tasks related to mapping an environment and localizing themselves. These tasks comprise the Simultaneous Localization and Mapping (SLAM) problem. Representation of knowledge related to the SLAM problem with a standard, flexible, and well-defined model, provides the base to develop efficient and interoperable solutions. As many existing works demonstrate, Semantic Web seems to be a clear approach, since they have formulated ontologies, as the base data model to represent such knowledge. In this article, we survey the most popular and recent SLAM ontologies with our aim being threefold: (i) propose a classification of SLAM ontologies according to the main knowledge needed to model the SLAM problem; (ii) identify existing ontologies for classifying, comparing, and contrasting them, in order to conceptualize SLAM domain for mobile robots; and (iii) pin-down lessons to learn from existing solutions in order to design better solutions and identify new research directions and further improvements. We compare the identified SLAM ontologies according to the proposed classification and, finally, we explore new data fields to enrich existing ontologies and highlight new possibilities in terms of performance and efficiency for SLAM solutions.