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Matching Annotated Generalized Voronoi Graphs for Autonomous Robot Localization and Mapping

We describe a localization and map building approach for a mobile robot which employs an annotated generalized Voronoi graph as its representation of the environment. The core of our approach is a matching scheme for solving the data association problem of identifying corresponding parts in a locally perceived Voronoi graph and the graph making up the robot’s map. We employ an edit distance approach for matching ordered trees and model the costs of adding or deleting branches based on a measure that assesses the stability of nodes and edges in the graphs as well as their relevance for navigation. In addition, we incorporate spatial constraints based on the graph annotations which leads to a top-down dynamic programming implementation that performs reliably and efficiently in practice.