Instant SLAM Initialization for Outdoor Omnidirectional Augmented Reality

Liu, Ruyu; Zhang, Jianhua; Yin, Kejie; Wu, Jiaxin; Lin, Ruihao; Chen, Shengyong

doi:10.1145/3205326.3205359

Cited by 10 publications

(2 citation statements)

References 5 publications

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“…However, most of the existing VSLAM -based solutions are implemented for indoor scenarios. Although the implementation of VSLAM in an outdoor LAR scenario is still limited, previous work by Liu et al [ 20 ] and Tang et al [ 21 ] have shed light on the potential of VSLAM in outdoor LAR applications, highlighting its capability to offer precise and robust localization and mapping. In addition to the possibility of improving localization performance, the VSLAM technique alone has several limitations, such as particular tracking loss and accumulating errors for building and localizing trajectory maps.…”

Section: Introductionmentioning

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

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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: Introductionmentioning

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

View full text Add to dashboard Cite

show abstract

“…Simultaneous localization and mapping (SLAM) greatly relies on its precision and robustness. To this end, many SLAM systems currently improved their performance by using depth cameras with external sensors, but this will cause inconvenience and hinder portability of the system. Therefore, a pure vision monocular SLAM system is still a considerable solution.…”

Section: Introductionmentioning

confidence: 99%

Robust simultaneous localization and mapping in low‐light environment

Huang

Liu

2019

Computer Animation & Virtual

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Complex and varied illumination makes computer vision research studies difficult. This research field pays much attention to scenes with weak illumination, especially in visual simultaneous localization and mapping (SLAM). Although the current feature‐based algorithm is mature, the existing SLAM method often fails because it cannot extract enough feature information in the low‐light environment. In this paper, we propose a new solution to this problem, which allows our system to work in environments with the majority of lighting. We propose a multifeature extraction algorithm to extract two kinds of image features simultaneously. With such a solution, our system can work when the single‐feature algorithm fails to extract enough feature points. We also add an image preprocessing step before tracking thread to cope with extremely dark conditions. Finally, we fully evaluate our approach on existing public data sets. Experiments show that the method combining multiple features can improve the robustness of the state‐of‐the‐art algorithm under weak illumination without affecting the real‐time performance.

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