SLAM-based Indoor Navigation in University Buildings

Sukhareva, E.S.; Tomchinskaya, T.N.; Serov, I.I.

doi:10.20948/graphicon-2021-3027-611-617

Cited by 2 publications

(1 citation statement)

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“…Compared to the multi-sensor method based on RTK-GPS+IMU, our proposed method exhibited significantly lower errors across different distance ranges, with an average reduction in error of approximately four-fold. This improvement was achieved by incorporating ARCore [55], a visual-inertial fusion method, which provides high-precision pose yaw angles. These yaw angles were then fused with high-precision RTK-GPS measurements.…”

Section: Testing Of Positional Alignment Results In Ar Geo-registrationmentioning

confidence: 99%

Accurate and Robust Rotation-Invariant Estimation for High-Precision Outdoor AR Geo-Registration

2023

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Geographic registration (geo-registration) is a crucial foundation for augmented reality (AR) map applications. However, existing methods encounter difficulties in aligning spatial data with the ground surface in complex outdoor scenarios. These challenges make it difficult to accurately estimate the geographic north orientation. Consequently, the accuracy and robustness of these methods are limited. To overcome these challenges, this paper proposes a rotation-invariant estimation method for high-precision geo-registration in AR maps. The method introduces several innovations. Firstly, it improves the accuracy of generating heading data from low-cost hardware by utilizing Real-Time Kinematic GPS and visual-inertial fusion. This improvement contributes to the increased stability and precise alignment of virtual objects in complex environments. Secondly, a fusion method combines the true-north direction vector and the gravity vector to eliminate alignment errors between geospatial data and the ground surface. Lastly, the proposed method dynamically combines the initial attitude relative to the geographic north direction with the motion-estimated attitude using visual-inertial fusion. This approach significantly reduces the requirements on sensor hardware quality and calibration accuracy, making it applicable to various AR precision systems such as smartphones and augmented reality glasses. The experimental results show that this method achieves AR geo-registration accuracy at the 0.1-degree level, which is about twice as high as traditional AR geo-registration methods. Additionally, it exhibits better robustness for AR applications in complex scenarios.

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Section: Testing Of Positional Alignment Results In Ar Geo-registrationmentioning

confidence: 99%