Abstract
Abstract. Indoor positioning technologies represent a fast developing field of research due to the rapidly increasing need for indoor location-based services (ILBS); in particular, for applications using personal smart devices. Recently, progress in indoor mapping, including 3D modeling and semantic labeling started to offer benefits to indoor positioning algorithms; mainly, in terms of accuracy. This work presents a method for efficient and robust indoor localization, allowing to support applications in large-scale environments. To achieve high performance, the proposed concept integrates two main indoor localization techniques: Wi-Fi fingerprinting and deep learning-based visual localization using 3D map. The robustness and efficiency of technique is demonstrated with real-world experiences.
Highlights
The need for indoor positioning systems is rapidly growing due to the emerging indoor commercial application market, including asset tracking, personal security and entertainment (Holman, 2012) with indoor locationbased services (ILBS), fueled by the proliferation of using personal smart devices
Since Wi-Fi fingerprinting positioning (WFP) is robust in complex indoor environment against non-line-of-sight (NLoS), signal fluctuation and multipath effect (He, 2015), we use WFP to provide a coarse estimation of the position as initial position estimation or as the final location when the visual algorithm fails
3.1.2 3D Indoor Map: The platform used for indoor 3D mapping is the LooMo robot with a Kinect V1 RGBD camera mounted on the top, see Figure 5, and data was collected at 10Hz
Summary
The need for indoor positioning systems is rapidly growing due to the emerging indoor commercial application market, including asset tracking, personal security and entertainment (Holman, 2012) with ILBS, fueled by the proliferation of using personal smart devices. To avoid using sensors not available in smart devices, in this work we integrated received signal strength (RSS)-based Wi-Fi fingerprinting positioning (WFP) with the InLoc. Since WFP is robust in complex indoor environment against non-line-of-sight (NLoS), signal fluctuation and multipath effect (He, 2015), we use WFP to provide a coarse estimation of the position as initial position estimation or as the final location when the visual algorithm fails. InLoc is a state-of-the-art visual indoor localization system, which can estimate 6DOF camera pose of a query image by using dense matching with an RGBD-based indoor map, including 3D model and image database. We improve the efficiency and robustness of InLoc by (1) applying Wi-Fi positioning results as initial information to significantly reduce the image retrieving search space as well as the map matching space; effective in large-scale environment, (2) the Wi-Fi positioning results can be offered to user as the final localization estimation when InLoc failed. More details of the methods are given in the experiment section
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