UMineAR: Mobile-Tablet-Based Abandoned Mine Hazard Site Investigation Support System Using Augmented Reality

Suh, Jangwon; Lee, Sang-Ho; Choi, Yosoon

doi:10.3390/min7100198

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…AT can help to locate the existing utilities and display the critical information. In mine site investigation, AT can help the user to quickly explore underground mine objects, making it easier to understand the subsurface environment [86].…”

Section: Augmented Territorymentioning

confidence: 99%

An Accurate and Efficient Quaternion-Based Visualization Approach to 2D/3D Vector Data for the Mobile Augmented Reality Map

Wang

Huang

Shi

2022

IJGI

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Increasingly complex vector map applications and growing multi-source spatial data pose a serious challenge to the accuracy and efficiency of vector map visualization. It is true especially for real-time and dynamic scene visualization in mobile augmented reality, with the dramatic development of spatial data sensing and the emergence of AR-GIS. Such issues can be decomposed into three issues: accurate pose representation, fast and precise topological relationships computation and high-performance acceleration methods. To solve these issues, a novel quaternion-based real-time vector map visualization approach is proposed in this paper. It focuses on precise position and orientation representation, accurate and efficient spatial relationships calculation and acceleration parallel rendering in mobile AR. First, a quaternion-based pose processing method for multi-source spatial data is developed. Then, the complex processing of spatial relationships is mapped into simple and efficient quaternion-based operations. With these mapping methods, spatial relationship operations with large computational volumes can be converted into efficient quaternion calculations, and then the results are returned to respond to the interaction. Finally, an asynchronous rendering acceleration mechanism is also presented in this paper. Experiments demonstrated that the method proposed in this paper can significantly improve vector visualization of the AR map. The new approach, when compared to conventional visualization methods, provides more stable and accurate rendering results, especially when the AR map has strenuous movements and high frequency variations. The smoothness of the user interaction experience is also significantly improved.

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Section: Augmented Territorymentioning

confidence: 99%

An Accurate and Efficient Quaternion-Based Visualization Approach to 2D/3D Vector Data for the Mobile Augmented Reality Map

Wang

Huang

Shi

2022

IJGI

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“…Furthermore, AR technology demonstrates immense potential in the field of underground engineering construction. In underground pipeline laying [9] and mining surveys [10], AR systems provide a visual perception of the underground space, reducing errors, risks, and enhancing work efficiency. The previous work by the authors [11] investigated reliable AR applications within a small-scale range using local calibration techniques with inertial sensors and monocular cameras.…”

Section: Introductionmentioning

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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“…In addition, there have been studies to measure environmental factors in underground mines using open-source hardware such as Arduino [14][15][16][17] and Raspberry Pi [18,19]. Studies have also been conducted to vividly visualize the workplace of underground mines or perform safety training using augmented reality (AR) [20,21] or virtual reality (VR) [22][23][24][25][26] technologies. A safety management system using artificial intelligence (AI) technology such as machine learning has also been developed [27,28].…”

Section: Introductionmentioning

confidence: 99%

Self-Driving Algorithm and Location Estimation Method for Small Environmental Monitoring Robot in Underground Mines

Kim¹,

Choi²

2021

Computer Modeling in Engineering &Amp; Sciences

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In underground mine environments where various hazards exist, such as tunnel collapse, toxic gases, the application of autonomous robots can improve the stability of exploration and efficiently perform repetitive exploratory operations. In this study, we developed a small autonomous driving robot for unmanned environmental monitoring in underground mines. The developed autonomous driving robot controls the steering according to the distance to the tunnel wall measured using the light detection and ranging sensor mounted on the robot to estimate its location by simultaneously considering the measured values of the inertial measurement unit and encoder sensors. In addition, the robot autonomously drives through the underground mine and performs environmental monitoring using the temperature/humidity, gas, and particle sensors mounted on the robot. As a result of testing the performance of the developed robot at an amethyst mine in Korea, the robot was found to be able to autonomously drive through tunnel sections with ∼28 m length, ∼2.5 m height, and ∼3 m width successfully. The average error of location estimation was approximately 0.16 m. Using environmental monitoring sensors, temperature of 15-17 • C, humidity of 42%-43%, oxygen concentration of 15.6%-15.7%, and particle concentration of 0.008-0.38 mg/m 3 were measured in the experimental area, and no harmful gases were detected. In addition, an environmental monitoring map could be created using the measured values of the robot's location coordinates and environmental factors recorded during autonomous driving.

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UMineAR: Mobile-Tablet-Based Abandoned Mine Hazard Site Investigation Support System Using Augmented Reality

Cited by 8 publications

References 12 publications

An Accurate and Efficient Quaternion-Based Visualization Approach to 2D/3D Vector Data for the Mobile Augmented Reality Map

An Accurate and Efficient Quaternion-Based Visualization Approach to 2D/3D Vector Data for the Mobile Augmented Reality Map

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

Self-Driving Algorithm and Location Estimation Method for Small Environmental Monitoring Robot in Underground Mines

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