Feature based CONDENSATION for mobile robot localization

Much attention has been given to CONDENSATION methods for mobile robot localization. This has resulted in somewhat of a breakthrough in representing uncertainty for mobile robots. In this paper we use CONDENSATION with planned sampling as a tool for doing feature based global localization in a large and semi-structured environment. This paper presents a comparison of four different feature types: sonar based triangulation points and point pairs, as well as lines and doors extracted using a laser scanner. We show experimental results that highlight the information content of the different features, and point to fruitful combinations. Accuracy, computation time and the ability to narrow down the search space are among the measures used to compare the features. From the comparison of the features, some general guidelines are drawn for determining good feature types.

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