A Survey on Indoor Positioning Systems: Foreseeing a Quality Design

Image data remains an important tool for post-event building assessment and documentation. After each natural hazard event, significant efforts are made by teams of engineers to visit the affected regions and collect useful image data. In general, a global positioning system (GPS) can provide useful spatial information for localizing image data. However, it is challenging to collect such information when images are captured in places where GPS signals are weak or interrupted, such as the indoor spaces of buildings. The inability to document the images’ locations hinders the analysis, organization, and documentation of these images as they lack sufficient spatial context. In this work, we develop a methodology to localize images and link them to locations on a structural drawing. A stream of images can readily be gathered along the path taken through a building using a compact camera. These images may be used to compute a relative location of each image in a 3D point cloud model, which is reconstructed using a visual odometry algorithm. The images may also be used to create local 3D textured models for building-components-of-interest using a structure-from-motion algorithm. A parallel set of images that are collected for building assessment is linked to the image stream using time information. By projecting the point cloud model to the structural drawing, the images can be overlaid onto the drawing, providing clear context information necessary to make use of those images. Additionally, components- or damage-of-interest captured in these images can be reconstructed in 3D, enabling detailed assessments having sufficient geospatial context. The technique is demonstrated by emulating post-event building assessment and data collection in a real building.

Section: Literature Review Of Path Reconstruction Techniquesmentioning

confidence: 99%

Automated Indoor Image Localization to Support a Post-Event Building Assessment

Liu

Dyke

Yeum

et al. 2020

“…Brena et al [ 25 ] used accuracy, coverage, and cost to compare the quality of location sensing systems. Ruiz et al [ 26 ] identified accuracy, precision, robustness, complexity, scalability, and cost as parameters that can be used to benchmark location sensing systems. Song et al [ 27 ] used range, accuracy, localization algorithm used and cost to compare location systems.…”

Section: Describing the Quality Of Location Information: Related Wmentioning

confidence: 99%

Location Information Quality: A Review

Ranasinghe

Kray

2018

The quality of location information is an important factor for location-based services (LBSs). In the literature, the quality of location information has been defined in different ways based on varying sets of aspects. The objectives of this paper are to review existing literature discussing location information quality and to provide a consistent framework for describing and dealing with location information quality. In particular, we review existing literature on different aspects of location information quality and on factors that affect location sensing technologies (and thus location information quality). Based on this review, we also propose a simple model for describing location information quality and a classification of the strategies for dealing with variations in the quality of location information. Designers of location sensing systems can use this model as a standard vocabulary for describing the quality of location information. The classification of strategies can be used by developers of LBSs apps to design alternative strategies for dealing with location information quality on three levels: sensor-level, algorithm-level, and application-level, which are aligned with the Location Stack model.

“…Although positioning technologies is widely argued in the past few years, developing a system that enables ubiquitous location determination is still open for researchers. Most of the research work attempts to integrate multiple sensors/system [ 28 ]. Various sources of information can be loosely integrated to estimate position and orientation of the device in a way that is more accurate and pervasive than any of the individual sensors.…”

Section: Location Determination Using Sensor-level Fusionmentioning

confidence: 99%

Context-Aware Personal Navigation Using Embedded Sensor Fusion in Smartphones

Saeedi

Moussa

El‐Sheimy

2014

Context-awareness is an interesting topic in mobile navigation scenarios where the context of the application is highly dynamic. Using context-aware computing, navigation services consider the situation of user, not only in the design process, but in real time while the device is in use. The basic idea is that mobile navigation services can provide different services based on different contexts—where contexts are related to the user's activity and the device placement. Context-aware systems are concerned with the following challenges which are addressed in this paper: context acquisition, context understanding, and context-aware application adaptation. The proposed approach in this paper is using low-cost sensors in a multi-level fusion scheme to improve the accuracy and robustness of context-aware navigation system. The experimental results demonstrate the capabilities of the context-aware Personal Navigation Systems (PNS) for outdoor personal navigation using a smartphone.