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Augmented Reality Indoor-Outdoor Navigation Through a Campus Digital Twin

2024· article· en· W4406499808 on OpenAlex
Mahmoud A. Abdelghany, Eleni Stroulia

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAugmented realityComputer scienceMixed realityHuman–computer interactionComputer graphics (images)MultimediaComputer vision

Abstract

fetched live from OpenAlex

Geolocated services can play a significant role in enhancing user experiences, in complex environments like complex campuses, for onboarding new employees and students and guiding tourists and visitors. This motivation is driving research and development in the area of geolocated augmented reality (AR) navigation but the field is still quite young. CampusGo is a software platform designed to provide a coherent suite of services to enable the development of AR-enabled, smart-campus applications, including (i) integration of a variety of multimodal data about the campus, such as building architectural diagrams and 3D models of buildings, people's profiles, offices and labs, landmarks and events; (ii) indoor and outdoor path-planning services; (iii) real-time navigation support; and (iv) engaging web and mobile front ends. In this paper, we present the overall system architecture and describe our early experience with deploying and beta-testing CampusGo on a university campus.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.824
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.243
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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