Diverse theories of animal navigation aim at explaining how to determine and maintain a course from one place to another in the environment, although each presents a particular perspective with its own terminologies. These vocabularies sometimes overlap, but unfortunately with different meanings. This paper attempts to define precisely the existing concepts and terminologies, so as to describe comprehensively the different theories and models within the same unifying framework. We present navigation strategies within a four-level hierarchical framework based upon levels of complexity of required processing (Guidance, Place recognition-triggered Response, Topological navigation, Metric navigation). This classification is based upon what information is perceived, represented and processed. It contrasts with common distinctions based upon the availability of certain sensors or cues and rather stresses the information structure and content of central processors. We then review computational models of animal navigation, i.e. of animats. These are introduced along with the underlying conceptual basis in biological data drawn from behavioral and physiological experiments, with emphasis on theories of "spatial cognitive maps". The goal is to aid in deriving algorithms based upon insights into these processes, algorithms that can be useful both for psychobiologists and roboticists. The main observation is, however, that despite the fact that all reviewed models claim to have biological inspiration and that some of them explicitly use "Cognitive Map"-like mechanisms, they correspond to different levels of our proposed hierarchy and that none of them exhibits the main capabilities of real "Cognitive Maps"--in Tolman's sense--that is, a robust capacity for detour and shortcut behaviors.