Search Results (125)

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

This study investigates how three technical factors—steering wheel tilt, torque, and cabin vibration frequency—affect driver posture. Heavy-truck drivers often suffer from musculoskeletal disorders (MSDs), mainly due to poor cabin ergonomics and prolonged postures during work. In countries like Ecuador, making major structural changes to cabin design is not feasible. These factors were identified through video analysis and surveys from drivers at two Ecuadorian trucking companies. An experimental system was developed using a simplified cabin to control these variables, while posture and muscle activity were recorded in 16 participants using motion capture, inertial sensors, and electromyography (EMG) on the upper trapezius, middle trapezius, triceps brachii, quadriceps muscle, and gastrocnemius muscle. The test protocol simulated key truck-driving tasks. Data were analyzed using ANOVA (${\displaystyle p<0.05}$), with technical factors and mass index as independent variables, and posture metrics as dependent variables. Results showed that head mass index significantly affected head abduction–adduction (${\displaystyle -}$8.12 to ${\displaystyle -}$2.18°), and spine mass index influenced spine flexion–extension (${\displaystyle -}$0.38 to 6.99°). Among technical factors, steering wheel tilt impacted trunk flexion–extension (${\displaystyle -}$13.56 to 16.99°) and arm rotation (${\displaystyle -}$31.1 to 19.7°). Steering wheel torque affected arm rotation (${\displaystyle -}$30.49 to ${\displaystyle -}$6.77°), while vibration frequency influenced forearm flexion–extension (3.76 to 16.51°). EMG signals showed little variation between muscles, likely due to the protocol’s short duration. These findings offer quantitative support for improving cabin ergonomics in low-resource settings through targeted, cost-effective design changes. Full article

Wireless indoor positioning systems are challenged by the reliance on densely deployed hardware and exhaustive site surveys, leading to elevated deployment and maintenance costs that limit scalability. This paper introduces a novel positioning framework that enhances the existing In-Building Wireless (IBW) infrastructure by retransmitting Global Navigation Satellite System (GNSS) signals. Pseudorange residuals extracted from raw GNSS measurements, when mapped against known cable lengths, facilitate anchor identification and precise ranging. In parallel, directional and inertial measurements are derived from the channel state information (CSI) of cellular reference signals. Building upon these observations, we develop a Hybrid Adaptive Filter-Graph Fusion (HAF-GF) algorithm for high-precision positioning, wherein the adaptive filter modulates observation noise based on Line-of-Sight (LoS) conditions, while a factor graph optimization over multiple positional constraints ensures global consistency and accelerates convergence. Ray tracing-based simulations in a complex office environment validate the efficacy of the proposed approach, demonstrating a 30% improvement in positioning accuracy and at least a threefold increase in deployment efficiency compared to conventional methods. Full article

The rapid measurement of three-dimensional bridge geometric shapes is crucial for assessing construction quality and in-service structural conditions. Existing geometric shape measurement methods predominantly rely on traditional surveying instruments, which suffer from low efficiency and are limited to sparse point sampling. This study proposes a novel framework that utilizes an airborne LiDAR–camera fusion system for data acquisition, reconstructs high-precision 3D bridge models through real-time mapping, and automatically extracts structural geometric shapes using deep learning. The main contributions include the following: (1) A synchronized LiDAR–camera fusion system integrated with an unmanned aerial vehicle (UAV) and a microprocessor was developed, enabling the flexible and large-scale acquisition of bridge images and point clouds; (2) A multi-sensor fusion mapping method coupling visual-inertial odometry (VIO) and Li-DAR-inertial odometry (LIO) was implemented to construct 3D bridge point clouds in real time robustly; and (3) An instance segmentation network-based approach was proposed to detect key structural components in images, with detected geometric shapes projected from image coordinates to 3D space using LiDAR–camera calibration parameters, addressing challenges in automated large-scale point cloud analysis. The proposed method was validated through geometric shape measurements on a concrete arch bridge. The results demonstrate that compared to the oblique photogrammetry method, the proposed approach reduces errors by 77.13%, while its detection time accounts for 4.18% of that required by a stationary laser scanner and 0.29% of that needed for oblique photogrammetry. Full article

Autonomous Underwater Vehicle (AUV) navigation relies on bottom-tracking velocity from Doppler Velocity Log (DVL) for positioning through dead-reckoning or aiding Strapdown Inertial Navigation System (SINS). In mid-water environments, the distance between the AUV and the seafloor exceeds the detection range of DVL, causing failure of bottom-tracking and leaving only water-relative velocity available. This makes unknown ocean currents a significant error source that leads to substantial cumulative positioning errors. This paper proposes a method for mid-water ocean current estimation using multibeam bathymetric survey data. First, the method models the regional unknown current field using radius basis functions (RBFs) and establishes an AUV dead-reckoning model incorporating the current field. The RBF model inherently satisfies ocean current incompressibility. Subsequently, by dividing the multibeam bathymetric point cloud data surveyed by the AUV into submaps and performing a terrain-matching algorithm, relative position observations among different AUV positions can be constructed. These observations are then utilized to estimate the RBF parameters of the current field within the navigation model. Numerical simulations and experiments based on real-world bathymetric and ocean current data demonstrate that the proposed method can effectively capture the complex spatial variations in ocean currents, contributing to the accurate reconstruction of the mid-water current field and significant improvement in positioning accuracy. Full article

In the age of digital twins, the digitalization of the urban environment is one of the key aspects in the optimization of urban management. The goal of urban digitalization is to provide a digital representation of physical infrastructure, data, information, and procedures for the management of complex anthropogenic systems. To meet this new goal, one must be able to understand the urban system through the integrated use of different methods in a multi-level approach. In this context, mobile surveying is a consolidated method for data collection in urban environments. A recent innovation, the mobile mapping system (MMS), is a versatile tool used to collect geospatial data efficiently, accurately, and quickly, with reduced time and costs compared to traditional survey methods. This system combines various technologies such as GNSS (global navigation satellite system), IMU (inertial measurement unit), LiDAR (light detection and ranging), and high-resolution cameras to map and create three-dimensional models of the surrounding environment. The aim of this study was to analyze the limitations, possible implementations, and the state of the art of MMSs for road infrastructure monitoring in order to create a DT (digital twin) for road infrastructure management, with a specific focus on extracting value-added information from a survey dataset. The case study presented here was part of the Turin Digital Twin project. In this context, an MMS was tested in a specific area to evaluate its potential and integration with other data sources, adhering to the multi-level and multi-sensor approach of the DT project. A key outcome of this work was the integration of the extracted information into a comprehensive geodatabase, transforming raw geospatial data into a structured tool that supports predictive maintenance and strategic road asset management toward DT implementation. Full article

Radio frequency (RF) spectrum sensing is critical for applications requiring precise object and posture detection and classification. This survey aims to provide a focused review of context-aware RF-based sensing, emphasizing its principles, advancements, and challenges. It specifically examines state-of-the-art techniques such as phased array radar, synthetic aperture radar, and passive RF sensing, highlighting their methodologies, data input domains, and spatial diversity strategies. The paper evaluates feature extraction methods and machine learning approaches used for detection and classification, presenting their accuracy metrics across various applications. Additionally, it investigates the integration of RF sensing with other modalities, such as inertial sensors, to enhance context awareness and improve performance. Challenges like environmental interference, scalability, and regulatory constraints are addressed, with insights into real-world mitigation strategies. The survey concludes by identifying emerging trends, practical applications, and future directions for advancing RF sensing technologies. Full article

As the exploration of marine resources continues to deepen, the utilization of Autonomous Underwater Vehicles (AUVs) for conducting marine resource surveys and underwater environmental mapping has become a common practice. In order to successfully accomplish exploration missions, AUVs require high-precision underwater navigation information as support. A Strapdown Inertial Navigation System (SINS) can provide AUVs with accurate attitude and heading information, while a Doppler Velocity Log (DVL) is capable of measuring the velocity vector of the AUVs. Therefore, the integrated SINS/DVL navigation system can furnish the necessary navigational information required by an AUV. In response to the issue of DVL being susceptible to external environmental interference, leading to reduced measurement accuracy, this paper proposes an end-to-end deep-learning approach to enhance the accuracy of DVL velocity vector measurements. The utilization of the raw measurement data from an Inertial Measurement Unit (IMU), which includes gyroscopes and accelerometers, to assist the DVL in velocity vector estimation and to refine it towards the Global Positioning System (GPS) velocity vector, compensates for the external environmental interference affecting the DVL, therefore enhancing the navigation accuracy. To evaluate the proposed method, we conducted lake experiments using SINS and DVL equipment, from which the collected data were organized into a dataset for training and assessing the model. The research results show that the DVL vector predicted by our model can achieve a maximum improvement of 69.26% in terms of root mean square error and a maximum improvement of 78.62% in terms of relative trajectory error. Full article

This paper accompanies the initial public release of the EDI multi-modal SLAM dataset, a collection of long tracks recorded with a portable sensor package. These include two global shutter RGB camera feeds, LiDAR scans, as well as inertial and GNSS data from an RTK-enabled IMU-GNSS positioning module—both as satellite fixes and internally fused interpolated pose estimates. The tracks are formatted as ROS1 and ROS2 bags, with separately available calibration and ground truth data. In addition to the filtered positioning module outputs, a second form of sparse ground truth pose annotation is provided using independently surveyed visual fiducial markers as a reference. This enables the meaningful evaluation of systems that directly utilize data from the positioning module into their localization estimates, and serves as an alternative when the GNSS reference is disrupted by intermittent signals or multipath scattering. In this paper, we describe the methods used to collect the dataset, its contents, and its intended use. Full article

Unmanned aerial vehicle (UAV) systems have recently become essential for mapping, surveying, and three-dimensional (3D) modeling applications. These systems are capable of providing highly accurate products through integrated advanced technologies, including a digital camera, inertial measurement unit (IMU), and Global Navigation Satellite System (GNSS). UAVs are a cost-effective alternative to traditional aerial photogrammetry, and recent advancements demonstrate their effectiveness in many applications. In UAV-based photogrammetry, ground control points (GCPs) are utilized for georeferencing to enhance positioning precision. The distribution, number, and location of GCPs in the study area play a crucial role in determining the accuracy of photogrammetric products. This research evaluates the accuracy of positioning techniques for image acquisition for photogrammetric production and the effect of GCP distribution models. The camera position was determined using real-time kinematic (RTK), post-processed kinematic (PPK), and precise point positioning-ambiguity resolution (PPP-AR) techniques. In the criteria for determining the GCPs, six models were established within the İstanbul Technical University, Ayazaga Campus. To assess the accuracy of the points in these models, the horizontal, vertical, and 3D root mean square error (RMSE) values were calculated, holding the test points stationary in place. In the study, 2.5 cm horizontal RMSE and 3.0 cm vertical RMSE were obtained with the model containing five homogeneous GCPs by the indirect georeferencing method. The highest RMSE values of all three components in RTK, PPK, and PPP-AR methods were obtained without GCPs. For all six models, all techniques have an error value of sub-decimeter. The PPP-AR technique yields error values that are comparable to those of the other techniques. The PPP-AR appears to be an alternative to RTK and PPK, which usually require infrastructure, labor, and higher costs. Full article

Deteriorating road infrastructure is a global concern, especially in low-income countries where financial and technological constraints hinder effective monitoring and maintenance. Traditional methods, like inertial profilers, are expensive and complex, making them unsuitable for large-scale use. This paper explores the integration of cost-effective, scalable smartphone technologies for road surface monitoring. Smartphone sensors, such as accelerometers and gyroscopes, combined with data preprocessing techniques like filtering and reorientation, improve the quality of collected data. Machine learning algorithms, particularly CNNs, are utilized to classify road anomalies, enhancing detection accuracy and system efficiency. The results demonstrate that smartphone-based systems, paired with advanced data processing and machine learning, significantly reduce the cost and complexity of traditional road surveys. Future work could focus on improving sensor calibration, data synchronization, and machine learning models to handle diverse real-world conditions. These advancements will increase the accuracy and scalability of smartphone-based monitoring systems, particularly for urban areas requiring real-time data for rapid maintenance. Full article

In 2013, a Ms 6.6 earthquake occurred at the boundary of Min County and Zhang County, triggering numerous landslides. Notably, two landslides with significantly different sliding characteristics emerged less than 100 m apart in Yongguang Village, Min County. The eastern landslide was characterized by instability induced by seismic inertial forces, whereas the western landslide exhibited flow slides triggered by liquefaction in loess. To further analyze the causes of these landslides, this study employed a 1 m depth ground temperature survey to probe the shallow groundwater in the area, aiming to understand the distribution of shallow groundwater. Based on the results from the 1 m depth ground temperature survey, a random forest model was applied to regressively predict the initial groundwater levels. The TRIGRS model was utilized to evaluate the influence of pre-earthquake rainfall conditions on landslide stability, and the pore water pressure outputs from TRIGRS were integrated with the Scoops3D model to analyze landslide stability under seismic effects. The results indicate that the combination of the 1 m depth ground temperature survey with high-density electrical methods and random forest approaches effectively captures the initial groundwater levels across the region. Notably, the heavy rainfall occurring one day prior to the earthquake did not significantly reduce the stability of the landslide in Yongguang Village. Instead, the abundant groundwater in the source area of the western landslide, combined with several months of pre-earthquake rainfall, resulted in elevated groundwater levels that created favorable conditions for its occurrence. While the primary triggering factor for both landslides in Yongguang Village was the earthquake, the distinct topographic and groundwater conditions led to significantly different sliding characteristics under seismic influence at the same slope. Full article

Underwater unmanned vehicles are complementary with human presence and manned vehicles for deeper and more complex environments. An autonomous underwater vechicle (AUV) has automation and long-range capacity compared to a cable-guided remotely operated vehicle (ROV). Navigation of AUVs is challenging due to the high absorption of radio-frequency signals underwater and the absence of a global navigation satellite system (GNSS). As a result, most navigation algorithms rely on inertial and acoustic signals; precise localization is then costly in addition to being independent from acoustic data communication. The purpose of this paper is to propose and analyze the performance of a novel low-cost simultaneous communication and localization algorithm. The considered scenario consists of an AUV that acoustically sends sensor or status data to a single fixed beacon. By estimating the Doppler shift and the range from this data exchange, the algorithm can provide a location estimate of the AUV. Using a robust state estimator, we analyze the algorithm over a survey path used for AUV mission planning both in numerical simulations and at-sea experiments. Full article

Autonomous Underwater Vehicles (AUVs) are widely used for hydrological monitoring, underwater exploration, and geological surveys. However, AUVs face limitations in underwater navigation due to the high costs associated with Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL), hindering the development of low-cost vehicles. Micro Electro Mechanical System Inertial Measurement Units (MEMS IMUs) are widely used in industry due to their low cost and can output acceleration and angular velocity, making them suitable as an Attitude Heading Reference System (AHRS) for low-cost vehicles. However, poorly calibrated MEMS IMUs provide an inaccurate angular velocity, leading to rapid drift in orientation. In underwater environments where AUVs cannot use GPS for position correction, this drift can have severe consequences. To address this issue, this paper proposes Underwater Gyros Denoising Net (UGDN), a method based on dilated convolutions and LSTM that learns and extracts the spatiotemporal features of IMU sequences to dynamically compensate for the gyroscope’s angular velocity measurements, reducing attitude and heading errors. In the experimental section of this paper, we deployed this method on a dataset collected from field trials and achieved significant results. The experimental results show that the accuracy of MEMS IMU data denoised by UGDN approaches that of fiber-optic SINS, and when integrated with DVL, it can serve as a low-cost underwater navigation solution. Full article

Autonomous Underwater Vehicles (AUVs) equipped with Side-Scan Sonar (SSS) play a critical role in seabed mapping, where precise navigation data are essential for mosaicking sonar images to delineate the seafloor’s topography and feature locations. However, the accuracy of AUV navigation, based on Strapdown Inertial Navigation System (SINS)/Doppler Velocity Log (DVL) systems, tends to degrade over long-term mapping, which compromises the quality of sonar image mosaics. This study addresses the challenge by introducing a post-processing navigation method for AUV SSS surveys, utilizing Factor Graph Optimization (FGO). Specifically, the method utilizes an improved Fourier-based image registration algorithm to generate more robust relative position measurements. Then, through the integration of these measurements with data from SINS, DVL, and surface Global Navigation Satellite System (GNSS) within the FGO framework, the approach notably enhances the accuracy of the complete trajectory for AUV missions. Finally, the proposed method has been validated through both the simulation and AUV marine experiments. Full article