A categorization of simultaneous localization and mapping knowledge for mobile robots

Autonomous robots are playing important roles in academic, technological, and scientific activities. Thus, their behavior is getting more complex. The main tasks of autonomous robots include mapping an environment and localize themselves. These tasks comprise the Simultaneous Localization and Mapping (SLAM) problem. Representation of the SLAM knowledge (e.g., robot characteristics, environment information, mapping and location information), with a standard and well-defined model, provides the base to develop efficient and interoperable solutions. However, as far as we know, there is not a common classification of such knowledge. Many existing works based on Semantic Web, have formulated ontologies to model information related to only some SLAM aspects, without a standard arrangement. In this paper, we propose a categorization of the knowledge managed in SLAM, based on existing ontologies and SLAM principles. We also classify recent and popular ontologies according to our proposed categories and highlight the lessons to learn from existing solutions.