Localization and building identification in outdoor environment for smartphone using integrated GPS and camera

Moun, Chhaily; Netramai, Chayakorn

doi:10.1109/dictap.2014.6821705

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…In vision-based techniques, images or video sequences of built-in cameras of mobile devices associated with computer vision and image-processing algorithms are used for pose estimation. Most vision-based techniques require a 3D model of the environment for camera pose estimation and tracking, which are called model-based tracking in computer vision [64], but there are also tracking approaches that require no previous information on the environment [84]. We also discuss these methods because they are of great interest in many current AR applications because of their robustness, accuracy, and speed.…”

Section: Vision-based Trackingmentioning

confidence: 99%

“…Template-matching approaches are efficient for poorly textured views and applications tracking specific objects in environments such as human body parts [65] and arm robots [66]. An online 3D template-matching algorithm that can reduce the operating time and the amount of data storage has been proposed by Moun et al [64]. The algorithm uses point cloud data sets with a reduced number of online-built templates and a matching function based on a correlation approach.…”

Section: Template Matchingmentioning

confidence: 99%

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A Survey of Marker-Less Tracking and Registration Techniques for Health & Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems

Sadeghi‐Niaraki

Choi

2020

Sensors

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Most existing augmented reality (AR) applications are suitable for cases in which only a small number of real world entities are involved, such as superimposing a character on a single surface. In this case, we only need to calculate pose of the camera relative to that surface. However, when an AR health or environmental application involves a one-to-one relationship between an entity in the real-world and the corresponding object in the computer model (geo-referenced object), we need to estimate the pose of the camera in reference to a common coordinate system for better geo-referenced object registration in the real-world. New innovations in developing cheap sensors, computer vision techniques, machine learning, and computing power have helped to develop applications with more precise matching between a real world and a virtual content. AR Tracking techniques can be divided into two subcategories: marker-based and marker-less approaches. This paper provides a comprehensive overview of marker-less registration and tracking techniques and reviews their most important categories in the context of ubiquitous Geospatial Information Systems (GIS) and AR focusing to health and environmental applications. Basic ideas, advantages, and disadvantages, as well as challenges, are discussed for each subcategory of tracking and registration techniques. We need precise enough virtual models of the environment for both calibrations of tracking and visualization. Ubiquitous GISs can play an important role in developing AR in terms of providing seamless and precise spatial data for outdoor (e.g., environmental applications) and indoor (e.g., health applications) environments.

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Section: Vision-based Trackingmentioning

confidence: 99%

Section: Template Matchingmentioning

confidence: 99%

A Survey of Marker-Less Tracking and Registration Techniques for Health & Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems

Sadeghi‐Niaraki

Choi

2020

Sensors

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“…Vision-Based: Model-based methods Most vision-based methods require a 3D model of the environment for camera pose estimation and tracking, which are called model-based tracking in computer vision [34]. Among these methods, edge-based tracking techniques require projecting a 3D geometric model (GIS or CAD, computer-aid design) on to an image and matching it with the picture's corresponding edge attributes.…”

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confidence: 99%

Assessment of 3D Models Placement Methods in Augmented Reality

et al. 2022

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Augmented reality (AR) is a relevant technology, which has demonstrated to be efficient for several applications, especially in the architecture, engineering, construction and operation (AECO) domain, where the integration of building information modeling (BIM) and AR has proved to be optimal in handling construction projects. However, the main challenge when integrating a virtual 3D model in an AR environment is the lack of precision and accuracy of placement that can occur between the real and the virtual environments. Although methods for placement via AR have been reported in the literature, there is a lack of investigations addressing their evaluation. Therefore, this paper proposes a methodology to perform a quantitative and qualitative assessment of several AR placement methods and a discussion about their usability in the specific context of AECO. We adopt root mean square error (RMSE) to quantify the placement accuracy of a 3D model and standard deviation to examine its stability (jittering). The results revealed that the AR placement error range is extremely wide (from a few centimeters up to meters). In marker-based methods, the results showed centimeter-range in both indoor and outdoor environments, compared to other methods (Inertial, Marker-less, etc.), while marker-less methods have widely varying error range from centimeters to a few meters. Other commercial solutions based on placement-sensors (GNSS and IMU), such as Trimble SiteVision, have proven placement performance in manual mode with centimeter order, while for the automatic mode, the order of placement and stability is metric, due to the low coverage of RTX (real time extended) in the study area.

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ARHome: Object Selection and Manipulation using Raycasting Technique with 3D-model Tracking in Handheld Augmented Reality

Halim

Ismail

2021

2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA)

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Localization and building identification in outdoor environment for smartphone using integrated GPS and camera

Cited by 4 publications

References 8 publications

A Survey of Marker-Less Tracking and Registration Techniques for Health & Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems

A Survey of Marker-Less Tracking and Registration Techniques for Health & Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems

Assessment of 3D Models Placement Methods in Augmented Reality

ARHome: Object Selection and Manipulation using Raycasting Technique with 3D-model Tracking in Handheld Augmented Reality

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