Utilizing High-level Visual Feature for Indoor Shopping Mall Navigation
This addresses the problem of convenient indoor navigation for shoppers in malls, though it appears incremental as it builds on existing visual recognition and mapping techniques.
The authors tackled indoor shopping mall navigation by developing a system that uses storefront images for localization and converts indicator maps into topological maps for navigation. Their system achieved robust localization and precise map generation in real shopping mall experiments.
Towards robust and convenient indoor shopping mall navigation, we propose a novel learning-based scheme to utilize the high-level visual information from the storefront images captured by personal devices of users. Specifically, we decompose the visual navigation problem into localization and map generation respectively. Given a storefront input image, a novel feature fusion scheme (denoted as FusionNet) is proposed by fusing the distinguishing DNN-based appearance feature and text feature for robust recognition of store brands, which serves for accurate localization. Regarding the map generation, we convert the user-captured indicator map of the shopping mall into a topological map by parsing the stores and their connectivity. Experimental results conducted on the real shopping malls demonstrate that the proposed system achieves robust localization and precise map generation, enabling accurate navigation.