Navigation and mapping in large-scale space

In a large-scale space, structure is at a significantly larger scale than the observations available at an instant. To learn the structure of a large-scale space from observations, the observer must build a cognitive map of the environment by integrating observations over an extended period of time, inferring spatial structure from perceptions and the effects of actions. The cognitive map representation of large-scale space must account for a mapping, or learning structure from observations, and navigation, or creating and executing a plan to travel from one place to another. Approaches to date tend to be fragile either because they don't build maps; or because they assume nonlocal observations, such as those available in preexisting maps or global coordinate systems, including active landmark beacons and geo-locating satellites. We propose that robust navigation and mapping systems for large-scale space can be developed by adhering to a natural, four-level semantic hierarchy of descriptions for representation, planning, and execution of plans in large-scale space. The four levels are sensorimotor interaction, procedural behaviors, topological mapping, and metric mapping. Effective systems represent the environment, relative to sensors, at all four levels and formulate robust system behavior by moving flexibly between representational levels at run time. We demonstrate our claims in three implemented models: Tour, the Qualnav system simulator, and the NX robot.