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Towards robust and convenient indoor shopping mall navigation, we propose anovel learning-based scheme to utilize the high-level visual information fromthe storefront images captured by personal devices of users. Specifically, wedecompose the visual navigation problem into localization and map generationrespectively. Given a storefront input image, a novel feature fusion scheme(denoted as FusionNet) is proposed by fusing the distinguishing DNN-basedappearance feature and text feature for robust recognition of store brands,which serves for accurate localization. Regarding the map generation, weconvert the user-captured indicator map of the shopping mall into a topologicalmap by parsing the stores and their connectivity. Experimental resultsconducted on the real shopping malls demonstrate that the proposed systemachieves robust localization and precise map generation, enabling accuratenavigation.

Utilizing High-level Visual Feature for Indoor Shopping Mall Navigation