Applications of 3D Reconstruction in Virtual Reality-Based Teleoperation: A Review in the Mining Industry
Alireza Kamran-Pishhesari,
Amin Moniri-Morad,
Javad Sattarvand
Abstract:Although multiview platforms have enhanced work efficiency in mining teleoperation systems, they also induce “cognitive tunneling” and depth-detection issues for operators. These issues inadvertently focus their attention on a restricted central view. Fully immersive virtual reality (VR) has recently attracted the attention of specialists in the mining industry to address these issues. Nevertheless, developing VR teleoperation systems remains a formidable challenge, particularly in achieving a realistic 3D mod… Show more
“…Furthermore, the swift proliferation of urbanization has resulted in unparalleled rates of population growth and urban expansion [17]. With the increasing migration of citizens from rural to urban areas in pursuit of economic prospects and better living conditions, cities are facing the challenge of absorbing expanding populations while preserving livability and quality of life [18].…”
Population growth and urbanization demand innovative strategies for sustainable city management. This paper focuses on the integration of the Internet of Things (IoT) and image processing technologies for environmental monitoring in sustainable urban development. The IoT forms an integral part of the Information and Communication Technology (ICT) infrastructure in smart sustainable cities. It offers a new model for urban design, due to the ability to offer environmentally sustainable alternatives. Furthermore, image processing is a method employed in computer vision that provides reliable approaches for extracting significant data from images. The convergence of these technologies has the capacity to enhance the effectiveness and durability of our urban surroundings. This paper discusses the current state-of-the-art in both IoT and image processing, highlighting their individual applications, architectures, and challenges. This paper explores the integration of the aforementioned technologies in a harmonized monitoring system to promote synergies and complementarities. Several case studies demonstrate the successful adoption of the harmonized approach in urban contexts, focusing on the environmental monitoring, energy management, transportation, and social wellbeing. The combination of IoT with image processing raises concerns regarding privacy, standardization, and scalability. The study has provided a direction for future research and suggested that more participant and multiple-strategy approaches could be beneficial to address some existing limitations and move toward a more sustainable urban context. It should therefore be viewed as a compass or a roadmap for future research in the areas of IoT and image processing-based monitoring towards todays and future sustainable urban environments.
“…Furthermore, the swift proliferation of urbanization has resulted in unparalleled rates of population growth and urban expansion [17]. With the increasing migration of citizens from rural to urban areas in pursuit of economic prospects and better living conditions, cities are facing the challenge of absorbing expanding populations while preserving livability and quality of life [18].…”
Population growth and urbanization demand innovative strategies for sustainable city management. This paper focuses on the integration of the Internet of Things (IoT) and image processing technologies for environmental monitoring in sustainable urban development. The IoT forms an integral part of the Information and Communication Technology (ICT) infrastructure in smart sustainable cities. It offers a new model for urban design, due to the ability to offer environmentally sustainable alternatives. Furthermore, image processing is a method employed in computer vision that provides reliable approaches for extracting significant data from images. The convergence of these technologies has the capacity to enhance the effectiveness and durability of our urban surroundings. This paper discusses the current state-of-the-art in both IoT and image processing, highlighting their individual applications, architectures, and challenges. This paper explores the integration of the aforementioned technologies in a harmonized monitoring system to promote synergies and complementarities. Several case studies demonstrate the successful adoption of the harmonized approach in urban contexts, focusing on the environmental monitoring, energy management, transportation, and social wellbeing. The combination of IoT with image processing raises concerns regarding privacy, standardization, and scalability. The study has provided a direction for future research and suggested that more participant and multiple-strategy approaches could be beneficial to address some existing limitations and move toward a more sustainable urban context. It should therefore be viewed as a compass or a roadmap for future research in the areas of IoT and image processing-based monitoring towards todays and future sustainable urban environments.
“…Drill and blast activities impact all these processes in a mine, influencing shovel productivity, equipment maintenance, highwall stability, ore recovery, and the related costs across all these processes. Therefore, it is necessary to utilize novel technologies, such as sensors, unmanned aerial vehicle (UAV), image processing, virtual reality, and teleoperation to address these issues [1][2][3][4].…”
Identifying the as-drilled location of blastholes is crucial for achieving optimal blasting results. This research proposes a novel integrated methodology to control drilling accuracy in open-pit mines. This approach is developed by combining aerial drone images with machine learning techniques. The study investigates the viability of photogrammetry combined with machine learning techniques, particularly Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), for automatically detecting blastholes in photogrammetry representations of blast patterns. To verify the hypothesis that machine learning can detect blastholes in images as effectively as humans, various datasets (drone images) were obtained from different mine sites in Nevada, USA. The images were processed to create photogrammetry mapping of the drill patterns. In this process, thousands of patches were extracted and augmented from the photogrammetry representations. Those patches were then used to train and test different CNN architectures optimized to locate blastholes. After reaching an acceptable level of accuracy during the training process, the model was tested using a piece of completely unknown data (testing dataset). The high recall, precision, and percentage of detected blastholes prove that the combination of SVM, CNN, and photogrammetry (PHG) is an effective methodology for detecting blastholes on photogrammetry maps.
“…Overcoming the challenge of achieving low-latency transmission of high-definition videos using 5G technology is considered a primary concern for unmanned driving in mines [15]. Currently, conventional 5G primarily assigns time slots for eMBB (Enhanced Mobile Broadband) [16], with a standard downlink allocation of 70% and an uplink allocation of 30%, which proves inadequate for extensive uplink scenarios, particularly high-definition video transmission from underground to the surface [17]. Furthermore, unmanned vehicles require collaboration with tunnel infrastructures during one-dimensional movement to offload computing tasks; however, the existing coordination algorithms [18,19] lack optimization for overall low latency.…”
In the development of intelligent mines, unmanned driving transportation has emerged as a key technology to reduce human involvement and enable unmanned operations. The operation of unmanned vehicles in mining environments relies on remote operation, which necessitates the low-latency transmission of high-definition video data across multiple channels for comprehensive monitoring and precise remote control. To address the challenges associated with unmanned driving in mines, we propose a comprehensive scheme that leverages the capabilities of 5G super uplink, edge collaborative computing, and advanced video transmission strategies. This approach utilizes dual-frequency bands, specifically 3.5 GHz and 2.1 GHz, within the 5G super uplink framework to establish an infrastructure designed for high-bandwidth and low-latency information transmission, crucial for real-time autonomous operations. To overcome limitations due to computational resources at terminal devices, our scheme incorporates task offloading and edge computing methodologies to effectively reduce latency and enhance decision-making speed for real-time autonomous activities. Additionally, to consolidate the benefits of low latency, we implement several video transmission strategies, such as optimized network usage, service-specific wireless channel identification, and dynamic frame allocation. An experimental evaluation demonstrates that our approach achieves an uplink peak rate of 418.5 Mbps with an average latency of 18.3 ms during the parallel transmission of seven channels of 4K video, meeting the stringent requirements for remote control of unmanned mining vehicles.
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