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Visual localization in crowded dynamic environments requires information about static and dynamic objects. This paper presents a robust method that learns the useful features from multiple runs in highly crowded urban environments. Useful features are identified as distinctive ones that are also reliable to extract in diverse imaging conditions. Relative importance of features is used to derive the weight for each feature. The popular Bag-of-words model is used for image retrieval anddoi:10.1109/iros.2013.6696749 dblp:conf/iros/HafezSKJ13 fatcat:3im46xp6iveybandu7b6326lre