Markov Localization using Correlation

Localization is one of the most important capabilities for autonomous mobile agents. Markov Localization (ML), applied to dense range images, has proven to be an effective technique. But its computational and storage requirements put a large burden on robot systems, and make it difficult to update the map dynamically. In this paper we introduce a new technique, based on correlation of a sensor scan with the map, that is several orders of magnitude more efficient than ML. CBML (correlation-based ML) permits video-rate localization using dense range scans, dynamic map updates, and a more precise error model than ML. In this paper we present the basic method of CBML, and validate its efficiency and correctness in a series of experiments on an implemented mobile robot base.

Authors

• Kurt Konolige
• Ken Chou

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