Performance Evaluation of Incremental Eigenspace Models for Mobile Robot Localization

摘要

We address the problem of robot localization based on eigenspace representations of a collection of views of the workspace, often referred to as appearance based methods. We study several approaches to build such eigenspaces in an incremental fashion (subspace tracking) , thereby allowing the simultaneous localization and mapping of a mobile vehicle. We see how different ways of determining whether or not new information (images) should be included in the eigenspace model lead to di#erent error values and model size (complexity). The performance evaluation allows us to know which criterion and parameters to use in a specific application to meet error specifications subject to computational constraints.