A self-localization system with global error reduction and online map-building capabilities

Abstract

An economical self-localization system which uses a monocular camera and a set of artificial landmarks is presented herein. The system represents the surrounding environment as a topological graph where each node corresponds to an artificial landmark and each edge corresponds to a relative pose between two landmarks. The edges are weighted based on an error metric (related to pose uncertainty) and a shortest path algorithm is applied to the map to compute the path corresponding to the least aggregate weight. This path is used to localize the camera with respect to a global coordinate system whose origin lies on an arbitrary reference landmark (i.e., the destination node of the path). The proposed system does not require a preliminary training process, as it builds and updates the map online. Experimental results demonstrate the performance of the system in reducing the global error associated with large-scale localization. © Springer-Verlag Berlin Heidelberg 2012.