Robot Cognitive Mapping – A Role for a Global Metric Map in a Cognitive Mapping Process

Abstract

In robotics it would be argued that we are closing the loop in a topological map using a global metric map. Drawing on our studies of human and animal cognitive mapping we proposed that a cognitive map comprises a topological map of metric local space representations [24]. Each local space defines a part of the environment that appears to enclose the animal/robot. Recently our Pioneer 2 robot has been computing such a map during its travels around our department. The advantage of such a map for a robot is that cumulative positional error is constrained to the local representation. Simpler localisation methods will often suffice for the local environment as global metric consistency is not required. The trade-off is that one cannot easily detect that one is re-entering a previously visited part of the environment via a new route (i.e. closing a loop) as is the case with a global metric map. The question we asked was: could we combine the local and global representations, exploiting the advantages of both - local representations for simpler localisation and no global metric consistency; global representation for easy loop detection. While a simple localisation method suffices for the local representation it would be inadequate for a global metric map. However the error could not grow unbounded if it were to be useful in the task of detecting loops. Our solution was to limit the size of the global map and have it move with the robot as it traversed its environment. We will describe the implementation of such a map and show that it can detect loops over a reasonable distance.