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• W2169733501 abstract "The work described in this paper concerns the problem of detecting loop-closure situations whenever an autonomous vehicle returns to previously visited places in the navigation area. An appearance-based perspective is considered by using images gathered by the on-board vision sensors for navigation tasks in heterogeneous environments characterized by the presence of buildings and urban furniture together with pedestrians and different types of vegetation. We propose a novel probabilistic on-line weight updating algorithm for the bag-of-words description of the gathered images which takes into account both prior knowledge derived from an off-line learning stage and the accuracy of the decisions taken by the algorithm along time. An intuitive measure of the ability of a certain word to contribute to the detection of a correct loop-closure is presented. The proposed strategy is extensively tested using well-known datasets obtained from challenging large-scale environments which emphasize the large improvement on its performance over previously reported works in the literature." @default.
• W2169733501 created "2016-06-24" @default.
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• W2169733501 date "2011-09-01" @default.
• W2169733501 modified "2023-10-16" @default.
• W2169733501 title "Adaptive appearance based loop-closing in heterogeneous environments" @default.
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• W2169733501 doi "https://doi.org/10.1109/iros.2011.6094537" @default.
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