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- W2029042833 abstract "This study introduces a method of general feature extraction for building a map and localization of a mobile robot using only sparsely sampled sonar data. Sonar data are acquired by using a general fixed-type sensor ring that frequently provides false returns on the locations of objects. We first suggest a data association filter that can classify sets of sonar data that are associated with the same hypothesized feature into one group. A feature extraction method is then introduced to decide the exact geometric parameters of the hypothesized feature in the group. We also show the possibility of extracting a circle feature consistently as well as a line or a point feature by using the proposed filter. These features are then assembled to build a global map and applied to extended Kalman filter-based localization of the robot. We demonstrate the validity of the proposed filter with the results of mapping and localization produced by real experiments." @default.
- W2029042833 created "2016-06-24" @default.
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- W2029042833 date "2009-01-01" @default.
- W2029042833 modified "2023-09-27" @default.
- W2029042833 title "General Feature Extraction for Mapping and Localization of a Mobile Robot Using Sparsely Sampled Sonar Data" @default.
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- W2029042833 doi "https://doi.org/10.1163/016918609x12496339865491" @default.
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