An Investigation of Running Load Comparisons of ARCore on Native Android and Unity for Outdoor Navigation System Using Smartphone

Brata, Komang Candra; Funabiki, Nobuo; Sukaridhoto, Sritrusta; Fajrianti, Evianita Dewi; Mentari, Mustika

doi:10.1109/icvee59738.2023.10348201

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…In [ 41 ], we conducted the preliminary implementation of the ARCore engine with the sensor fusion approach both in Android native and Unity for the outdoor navigation system. This LAR application integrates the sensor fusion (GPS and compasses) with the VSLAM technique.…”

Section: Previous Workmentioning

confidence: 99%

An Enhancement of Outdoor Location-Based Augmented Reality Anchor Precision through VSLAM and Google Street View

Brata,

Funabiki,

Panduman

et al. 2024

Sensors

Self Cite

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Outdoor Location-Based Augmented Reality (LAR) applications require precise positioning for seamless integrations of virtual content into immersive experiences. However, common solutions in outdoor LAR applications rely on traditional smartphone sensor fusion methods, such as the Global Positioning System (GPS) and compasses, which often lack the accuracy needed for precise AR content alignments. In this paper, we introduce an innovative approach to enhance LAR anchor precision in outdoor environments. We leveraged Visual Simultaneous Localization and Mapping (VSLAM) technology, in combination with innovative cloud-based methodologies, and harnessed the extensive visual reference database of Google Street View (GSV), to address the accuracy limitation problems. For the evaluation, 10 Point of Interest (POI) locations were used as anchor point coordinates in the experiments. We compared the accuracies between our approach and the common sensor fusion LAR solution comprehensively involving accuracy benchmarking and running load performance testing. The results demonstrate substantial enhancements in overall positioning accuracies compared to conventional GPS-based approaches for aligning AR anchor content in the real world.

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Section: Previous Workmentioning

confidence: 99%

An Enhancement of Outdoor Location-Based Augmented Reality Anchor Precision through VSLAM and Google Street View

Brata,

Funabiki,

Panduman

et al. 2024

Sensors

Self Cite

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Augmented Reality-Based Navigation: Bridging Indoor and Outdoor Environments through Immersal Integration

Maulana Achmad,

Sukaridhoto,

Agus Zainuddin

et al. 2024

2024 IEEE International Symposium on Consumer Technology (ISCT)

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Performance Investigations of VSLAM and Google Street View Integration in Outdoor Location-Based Augmented Reality under Various Lighting Conditions

Brata,

Funabiki,

Riyantoko

et al. 2024

Electronics

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The growing demand for Location-based Augmented Reality (LAR) experiences has driven the integration of Visual Simultaneous Localization And Mapping (VSLAM) with Google Street View (GSV) to enhance the accuracy. However, the impact of the ambient light intensity on the accuracy and reliability is underexplored, posing significant challenges in outdoor LAR implementations. This paper investigates the impact of light conditions on the accuracy and reliability of the VSLAM/GSV integration approach in outdoor LAR implementations. This study fills a gap in the current literature and offers valuable insights into vision-based approach implementation under different light conditions. Extensive experiments were conducted at five Point of Interest (POI) locations under various light conditions with a total of 100 datasets. Descriptive statistic methods were employed to analyze the data and assess the performance variation. Additionally, the Analysis of Variance (ANOVA) analysis was utilized to assess the impact of different light conditions on the accuracy metric and horizontal tracking time, determining whether there are significant differences in performance across varying levels of light intensity. The experimental results revealed that a significant correlation (p < 0.05) exists between the ambient light intensity and the accuracy of the VSLAM/GSV integration approach. Through the confidence interval estimation, the minimum illuminance 434 lx is needed to provide a feasible and consistent accuracy. Variations in visual references, such as wet surfaces in the rainy season, also impact the horizontal tracking time and accuracy.

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An Investigation of Running Load Comparisons of ARCore on Native Android and Unity for Outdoor Navigation System Using Smartphone

Cited by 3 publications

References 20 publications

An Enhancement of Outdoor Location-Based Augmented Reality Anchor Precision through VSLAM and Google Street View

An Enhancement of Outdoor Location-Based Augmented Reality Anchor Precision through VSLAM and Google Street View

Augmented Reality-Based Navigation: Bridging Indoor and Outdoor Environments through Immersal Integration

Performance Investigations of VSLAM and Google Street View Integration in Outdoor Location-Based Augmented Reality under Various Lighting Conditions

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