Estimating consistency of geometric world models through observation of a localization process

We present the geometric consistency filter, an approach to extraction of spatial information in the context of the navigation for autonomous mobile robots. The geometric consistency filter can detect a significant portion of local inconsistencies in geometric world models, which result from partially dynamic environments and a limited resolution of the sensing systems. Belief about consistency of portions of the world model is based on incremental fusion of evidence, which is obtained through analysis of the localization process. Presented experiments demonstrate how the estimation of consistency can be useful for robust localization and updating of world models in dynamic environments.