2016/10/06 by Ziwei Xu, Haitian Zheng, Xu, Ziwei +11
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #Robotics and Sensor-Based Localization #cs.CV
paper · pdf · doi:10.48550/arxiv.1610.01906
openalex publication_date 2016/10/06 · arxiv created 2017/02/18 · arxiv updated 2017/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.