Design of public space guide system based on augmented reality technology

With the rapid development of science and technology, augmented reality technology provides intelligent and application services. The research is based on imaging techniques using augmented reality technology and camera image capture. Then, it uses screen error algorithms and scale-invariant feature transformation operators to test the quality of scene spatial models. The experimental results demonstrated that the camera significantly improved the frame rate of scene model rendering and could steadily enhance rendering efficiency. For image quality and its influencing factors, binary robust invariant scalable keypoints and scale-invariant feature transformation algorithms in viewpoint changes had the highest recall of 92%. The map drawing module, Hessian matrix, and scale-invariant feature transformation algorithm in the image blurring test achieved the highest recall rate of 98%. This demonstrates the advantage of using a scale-invariant feature transformation operator to capture scene space influence and provide a more accurate spatial model reference for augmented reality technology. This enhances the functional design of the guide system.

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Publication Details

Journal
Discover Artificial Intelligence
Published
2025-07-24
DOI
https://doi.org/10.1007/s44163-025-00399-2
Primary Topic
Augmented Reality Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Design of public space guide system based on augmented reality technology

Lixin Ran, Pu Jiao
Discover Artificial Intelligence
Augmented Reality Applications
article

Design of public space guide system based on augmented reality technology

Lixin Ran, Pu Jiao
article en

Abstract

With the rapid development of science and technology, augmented reality technology provides intelligent and application services. The research is based on imaging techniques using augmented reality technology and camera image capture. Then, it uses screen error algorithms and scale-invariant feature transformation operators to test the quality of scene spatial models. The experimental results demonstrated that the camera significantly improved the frame rate of scene model rendering and could steadily enhance rendering efficiency. For image quality and its influencing factors, binary robust invariant scalable keypoints and scale-invariant feature transformation algorithms in viewpoint changes had the highest recall of 92%. The map drawing module, Hessian matrix, and scale-invariant feature transformation algorithm in the image blurring test achieved the highest recall rate of 98%. This demonstrates the advantage of using a scale-invariant feature transformation operator to capture scene space influence and provide a more accurate spatial model reference for augmented reality technology. This enhances the functional design of the guide system.

Discover Artificial IntelligenceVol. 5(1)
Mahasarakham University (TH), Anyang Institute of Technology (CN), Henan University of Economic and Law (CN)
Sustainable cities and communities
Openalex Percentile: Top 84%
Augmented Reality Applications
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Design of public space guide system based on augmented reality technology — Lixin Ran, Pu Jiao · Discover Artificial Intelligence (2025) | TGRS Research Map | TGRS