Many augmented reality (AR) systems are developed for entertainment, but AR and particularly mobile AR potentially have more application possibilities in other fields. For example, in civil engineering or city planning, AR could be used in combination with CityGML building models to enhance some typical workflows in planning, execution and operation processes. A concrete example is the geo-referenced on-site visualization of planned buildings or building parts, to simplify planning processes and optimize the communication between the participating decision-makers. One of the main challenges for the visualization lies in the pose tracking, i.e. the real-time estimation of the translation and rotation of the mobile device to align the virtual objects with reality. In this paper, we introduce a proof-of-concept fine-grained mobile AR CityGML-based pose tracking system aimed at the mentioned applications. The system estimates poses by combining 3D CityGML data with information derived from 2D camera images and an inertial measurement unit and is fully self-sufficient and operates without external infrastructure. The results of our evaluation show that CityGML and low-cost off-the-shelf mobile devices, such as smartphones, already provide performant and accurate mobile pose tracking for AR in civil engineering and city planning.
CityGML, a semantic information model for digital/virtual city models has become quite popular in various scenarios. While the data format is still actively under development, it is already supported by different software solutions, especially GIS-based desktop applications. Mobile systems on the other hand are still neglected, even though the georeferenced objects of CityGML have many application fields, for example, in the currently popular area of location-based Augmented Reality. In this paper we present an independent multi-platform CityGML viewer, its architecture and specific implementation techniques that we use to realize and optimize the process of visualizing CityGML data for use in Augmented Reality. The main focus lies in improving the implementation on mobile devices, such as smartphones, and assessing its usability and performance in comparison to web-based approaches. Due to the constrained hardware resources of smartphones, it is a particular challenge to handle complex 3D objects and large virtual worlds as provided by CityGML, not only in terms of memory and storage space, but also with respect to mobile processing units and display sizes.
Fingerprinting-based approaches are particularly suitable for deploying indoor positioning systems for pedestrians with minimal infrastructure costs. The accuracy of the method, however, strongly depends on the quality of collected labeled fingerprints within the calibration phase, which is a tedious process when done manually in a static fashion. We present VI-SLAM2tag, a system for auto-labeling of dynamically collected fingerprints using the visual-inertial simultaneous localization and mapping (VI-SLAM) module of ARCore. ARCore occasionally updates its internal coordinate system. Mapping the entire trajectory to a target coordinate system via a single transformation thus results in large drift effects. To solve this, we propose a strategy for determining locally optimal subtrajectory transformations. Our system is evaluated with respect to the accuracy of the generated position labels using a geodetic tracking system. We achieve an average labeling error of roughly 50 cm for trajectories of up to 15 minutes, which is sufficient for fingerprinting-based localization. We demonstrate this by collecting a multi-floor dataset including WLAN and IMU data and show how it can be used to train neural network based models that achieve a positioning accuracy of roughly 2 m. VI-SLAM2tag and the dataset are made publicly available.
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