Abstract
Accurate smartphone-based outdoor localization systems in deep urban canyons are increasingly needed for various IoT applications. As smart cities have developed, building information modeling (BIM) has become widely available. This article, for the first time, presents a semantic Visual Positioning System (VPS) for accurate and robust position estimation in urban canyons where the global navigation satellite system (GNSS) tends to fail. In the offline stage, a material segmented BIM is used to generate segmented images. In the online stage, an image is taken with a smartphone camera that provides textual information about the surrounding environment. The approach utilizes computer vision algorithms to segment between the different types of material class identified in the smartphone image. A semantic VPS method is then used to match the segmented generated images with the segmented smartphone image. Each generated image contains position information in terms of latitude, longitude, altitude, yaw, pitch, and roll. The candidate with the maximum likelihood is regarded as the precise position of the user. The positioning result achieved an accuracy of 2.0 m among high-rise buildings on a street, 5.5 m in a dense foliage environment, and 15.7 m in an alleyway. This represents an improvement in positioning of 45% compared to the current state-of-the-art method. The estimation of yaw achieved accuracy of 2.3°, an eight-fold improvement compared to the smartphone IMU.
Highlights
Urban localization is essential to the development of numerous IoT applications, such as the digital management of navigation, augmented reality, and commercial related services [1], and is an indispensable part of daily life due to its widespread application [2]
Three locations were selected in challenging deep urban canyons surrounded by tall buildings where global navigation satellite system (GNSS) signals are heavily reflected and blocked
The rotational performance the method was performance of the theof proposed method was analyzed analyzed based on the ideal smartphone image segmentation, compared to the smartphone based on the ideal smartphone image segmentation, compared to the smartphone onideal the ideal smartphone image segmentation, compared tosmartphone the smartphone basedbased on the smartphone image segmentation, compared to the as shown shown in Table as shown shown in Table
Summary
Urban localization is essential to the development of numerous IoT applications, such as the digital management of navigation, augmented reality, and commercial related services [1], and is an indispensable part of daily life due to its widespread application [2]. Significant improvement is needed in the positioning performance of GNSS in urban areas due to signal blockages and reflections caused by tall buildings and dense foliage [8]. In these environments, most signals are non-line-of-sight (NLOS), which can severely degrade the localization accuracy [9]. Most signals are non-line-of-sight (NLOS), which can severely degrade the localization accuracy [9] They cause large estimation errors if they are either treated as line-of-sight (LOS) or not used properly [10]. A review of state-of-the-art localization was published in 2018 [11] Each of these technologies has its own advantages and limitations. A pedestrian self-localization system should be sufficiently accurate and efficient to provide positioning information [12]
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