Search Results (91)

Accurate detection and positioning of road users are essential for vehicle-to-infrastructure (V2I)-assisted autonomous driving. For this purpose, the road user’s ground contact point is usually detected in a monocular camera image. Then, a homography-based method is used to convert this detected point into its corresponding map position. However, the homography-based method assumes that the ground is planar, which leads to significant positioning errors in real-world environments. This limitation degrades the reliability of V2I-assisted autonomous driving, particularly in environments with complex road geometries. This study presents a method for accurately estimating the positions of road users using 3D point clouds generated by a Mobile Mapping System (MMS) for map construction without incurring additional costs. Moreover, since surveillance cameras are typically installed in urban areas, point clouds for these regions are often already available. The proposed method uses a pre-generated Look-Up Table (LUT), which is created by projecting MMS-based 3D point clouds onto the image coordinate system, so that each pixel in the image stores its corresponding 3D map position. Once the ground contact points of road users are detected in the image, the corresponding 3D positions on the map can be directly obtained by referencing the LUT. In the experiments, the proposed method was evaluated using surveillance camera images and MMS-based point clouds collected from various real-world environments. The results show that the proposed method reduces positioning errors of road users by an average of 61.4% compared to the conventional homography-based method. The improvement is particularly significant in environments with ground slope variations. In addition, the proposed method demonstrates real-time feasibility on an embedded camera, achieving low latency and power-efficient performance suitable for V2I edge deployment. Full article

Sidewalk high-definition (HD) maps require centimetre-level representation of pedestrian barriers to support mobility assistance and barrier-free infrastructure management. This study evaluates six mobile light detection and ranging (LiDAR) platforms for sidewalk HD mapping: terrestrial laser scanning (TLS), a push-cart mobile mapping system (MMS), two backpack systems (GNSS/INS (Global Navigation Satellite System/Inertial Navigation System)-aided and SLAM (simultaneous localization and mapping)-based), and two handheld systems (GNSS/INS-aided and SLAM-based). Surveys were conducted at two sites with contrasting occlusion and GNSS conditions (park and dense downtown corridors). Point clouds were transformed to a common control network, with independent checkpoints for absolute accuracy. The reference dataset achieved a planimetric root mean square error (RMSE) of 0.017–0.049 m and vertical RMSE of 0.009–0.014 m across sites. Platforms were compared for positional accuracy, point density, and extractability of key accessibility attributes (effective width, step height, and longitudinal slope). Cart-mounted MMS provided stable geometry under occlusion, while SLAM-based handheld mapping improved robustness in GNSS-degraded areas; backpack SLAM performance depended on loop-closure opportunities and scene dynamics. We provide guidance on selecting pedestrian-scale LiDAR platforms for sidewalk HD mapping under different survey conditions. Full article

Precise modeling of narrow gorges is challenging due to extreme confinement, hindering visibility and accessibility. These environments often render Global Navigation Satellite Systems (GNSS)-based positioning unfeasible, a difficulty compounded by water and dense vegetation. Consequently, multi-technique data fusion is required. This study proposes a robust methodology to generate high-resolution 3D models of such complex environments by integrating multiple aerial (e.g., Unmanned Aerial Vehicles, UAVs) and terrestrial techniques. A multi-sensor approach combined UAV-Light Detection and Ranging (LiDAR) and UAV-photogrammetry for external areas with Terrestrial laser scanning (TLS), Mobile Mapping System (MMS), and Spherical Photogrammetry (SP) for the canyon floor. Furthermore, the representativeness of these 3D models was analyzed against standard Digital Terrain Models (DTMs) for determining water height levels during flood events. A one-dimensional hydraulic (1DH) model compared the 3D mesh approach with the traditional 2.5D perspective in a challenging, narrow canyon prone to flooding. Our results show that traditional 2.5D DTMs significantly over- or underestimate water levels in narrow sections—failing to account for overhangs and vertical wall irregularities—whereas high-resolution 3D meshes provide a more realistic representation of hydraulic behavior. This work demonstrates that multi-sensor data fusion is essential for accurate flood risk management and infrastructure planning in complex fluvial environments. Full article

Large-scale terrestrial laser scanning (TLS) point clouds are increasingly used for applications such as digital twins and cultural heritage documentation; however, removing unwanted human points captured during acquisition remains a largely manual and time-consuming process. This study proposes a geometry-aware framework for automatically removing human noise from TLS point clouds by projecting 2D instance segmentation masks (obtained using You Only Look Once (YOLO) v8 with an instance segmentation head) into 3D space and validating candidates through multi-stage geometric filtering. To suppress false positives induced by reprojection misalignment and planar background structures (e.g., walls and ground), we introduce projection-followed geometric validation (or “geometric gating”) using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and principal component analysis (PCA)-based planarity analysis, followed by cluster-level plausibility checks. Experiments were conducted on two real-world outdoor TLS datasets—(i) Osaka Metropolitan University Sugimoto Campus (OMU) (82 scenes) and (ii) Jinaimachi historic district in Tondabayashi (JM) (68 scenes). The results demonstrate that the proposed method achieves high noise removal accuracy, obtaining precision/recall/intersection over union (IoU) of 0.9502/0.9014/0.8607 on OMU and 0.8912/0.9028/0.8132 on JM. Additional experiments on mobile mapping system (MMS) data from the Waymo Open Dataset demonstrate stable performance without parameter recalibration. Furthermore, quantitative and qualitative comparisons with representative time-series geometric dynamic object removal methods, including DUFOMap and BeautyMap, show that the proposed approach maintains competitive recall under a human-only ground-truth definition while reducing over-removal of static structures in TLS scenes, particularly when humans are observed in only one or a few scans due to limited revisit frequency. The end-to-end processing time with YOLOv8 was 935.62 s for 82 scenes (11.4 s/scene) on OMU and 571.58 s for 68 scenes (8.4 s/scene) on JM, supporting practical efficiency on high-resolution TLS imagery. Ablation studies further clarify the role of each stage and indicate stable performance under the observed reprojection errors. The annotated human point cloud dataset used in this study has been publicly released to facilitate reproducibility and further research on human noise removal in large-scale TLS scenes. Full article

Digital twins (DTs) are increasingly adopted to enhance the monitoring, management, and planning of urban infrastructure. While DT development for buildings is well established, applications to urban road networks remain limited, particularly in integrating heterogeneous geospatial datasets into semantically rich, multi-scale representations. This study presents a methodology for developing a BIM-based DT of urban roads by integrating geospatial data from Mobile Mapping System (MMS) surveys with semantic information from municipal geodatabases. The approach follows a multi-modal (point clouds, imagery, vector data), multi-scale and multi-level framework, where ‘multi-level’ refers to modeling at different scopes—from a city-wide level, offering a generalized representation of the entire road network, to asset-level detail, capturing parametric BIM elements for individual road segments or specific components such as road sign and road marker, lamp posts and traffic light. MMS-derived LiDAR point clouds allow accurate 3D reconstruction of road surfaces, curbs, and ancillary infrastructure, while municipal geodatabases enrich the model with thematic layers including pavement condition, road classification, and street furniture. The resulting DT framework supports multi-scale visualization, asset management, and predictive maintenance. By combining geometric precision with semantic richness, the proposed methodology delivers an interoperable and scalable framework for sustainable urban road management, providing a foundation for AI-ready applications such as automated defect detection, traffic simulation, and predictive maintenance planning. The resulting DT achieved a geometric accuracy of ±3 cm and integrated more than 45 km of urban road network, enabling multi-scale analyses and AI-ready data fusion. Full article

Frequent heavy rainfalls due to climate change in recent years have led to an increasing incidence of severe damage, such as levee breaches. However, the integrity of levees is currently assessed by visual inspection, relying on the skill and experience of the overseeing engineers. Future work requires close monitoring of the external shape of levees and the implementation of quantitative assessments if abnormalities such as deformation are discovered. Therefore, the mobile mapping system (MMS), which uses a vehicle-mounted laser scanner to conduct surveys while moving, has attracted attention as a method for conducting high-precision surveys. However, the presence of blind spots in the laser irradiation indicates that there is no practical method for identifying areas that require countermeasures for the entire levee. In this paper, we discuss the appropriate position of laser irradiation that allows data acquisition down to the toe of the slope, and then propose a method of laser irradiation from a high altitude. Compared to previous laser surveys using vehicles, this method was able to obtain a high-density laser point cloud over the entire levee, demonstrating that it is possible to detect detailed deformations not only on the crest of the levee but also on the slope. Full article

This paper presents an intelligent robotic system for in-pipe inspection that integrates a novel mechanical design, deep learning-based defect detection, and high-fidelity simulation for real-time validation. Unlike existing solutions, the proposed system combines a Mecanum wheel-based mobile platform with a modular arm and advanced pan-tilt camera, enabling navigation and inspection of pipes ranging from 100 mm to 500 mm in diameter. A comprehensive dataset of 53,486 images, including 27,000 annotated defect instances across six critical classes, was used to train a YOLOv11-based detection framework. The model achieved high accuracy with a precision of 0.9, recall of 0.8, mAP@0.5 of 0.9, and mAP@0.5:0.95 of 0.6, outperforming previous YOLO versions, SSD, RCNN, and DinoV2 by 26% in mAP. Real-time testing on a Raspberry Pi Camera 3 Wide IR module validated the robust detection under realistic conditions. This work contributes a mechanically adaptable robot, an optimized deep learning inspection framework, and an integrated simulation-to-deployment workflow, providing a scalable and autonomous solution for industrial pipeline inspection. Full article

Mobile Mapping Systems (MMSs) have emerged as indispensable instruments for producing high-precision road maps in recent years. Despite incorporating modern devices, their efficacy may be influenced by operational variables such as vehicle speed or the type of GNSS (Global Navigation Satellite System) differential correction employed. This study assesses the impact of varying vehicle speeds and differential correction settings on the accuracy of point grids acquired with an MMS on a two-lane rural road. The experiment was performed across a 7 km distance, incorporating two speeds (40 and 60 km/h) and two travel directions. Three correction methodologies were examined: a proximate local base (MBS), a network station solution of the National Geographic Institute (NET), and virtual reference stations (VRSs). The methodology encompassed normality analysis, descriptive statistics, mean comparisons, one- and two-factor analysis of variance (ANOVA), and the computation of the root mean square error (RMSE) as a measure of accuracy. The findings indicate that horizontal discrepancies remain steady and unaffected by the correction technique; however, notable changes are seen in the vertical component, with the NET option proving to be the most effective. The acquisition rate is the primary determinant, exacerbating errors at 60 km/h. In conclusion, the dependability of MMS surveys is contingent upon the correction approach and operational conditions, and it is advisable to sustain moderate speeds to guarantee precise three-dimensional models. Full article

The application of autonomous navigation in intelligent agriculture is becoming more and more extensive. Traditional navigation schemes in greenhouses, orchards, and other agricultural environments often have problems such as the inability to deal with an uneven illumination distribution, complex layout, highly repetitive and similar structures, and difficulty in receiving GNSS (Global Navigation Satellite System) signals. In order to solve this problem, this paper proposes a new tightly coupled LiDAR (Light Detection and Ranging) inertial odometry SLAM (LIO-SAM) framework named April-LIO-SAM. The framework innovatively uses Apriltag, a two-dimensional bar code widely used for precise positioning, pose estimation, and scene recognition of objects as a global positioning beacon to replace GNSS to provide absolute pose observation. The system uses three-dimensional LiDAR (VLP-16) and IMU (inertial measurement unit) to collect environmental data and uses Apriltag as absolute coordinates instead of GNSS to solve the problem of unreliable GNSS signal reception in greenhouses, orchards, and other agricultural environments. The SLAM trajectories and navigation performance were validated in a carefully built greenhouse and orchard environment. The experimental results show that the navigation map developed by the April-LIO-SAM yields a root mean square error of 0.057 m. The average positioning errors are 0.041 m, 0.049 m, 0.056 m, and 0.070 m, respectively, when the density of Apriltag is 3 m, 5 m, and 7 m. The navigation experimental results indicate that, at speeds of 0.4, 0.3, and 0.2 m/s, the average lateral deviation is less than 0.053 m, with a standard deviation below 0.034 m. The average heading deviation is less than 2.3°, with a standard deviation below 1.6°. The positioning stability experiments under interference conditions such as illumination and occlusion were carried out. It was verified that the system maintained a good stability under complex external conditions, and the positioning error fluctuation was within 3.0 mm. The results confirm that the robot positioning and navigation accuracy of mobile robots satisfy the continuity in the facility. Full article

Technological development has strongly impacted all processes related to the design, construction, and management of real estate assets. In fact, the introduction of the BIM approach has required the application of three-dimensional survey technologies, and in particular the use of LiDAR instruments, both in their static (TLS—terrestrial laser scanner) and dynamic (iMMS—indoor mobile mapping system) implementations. Operators and developers of LiDAR technologies, for the implementation of scan-to-BIM procedures, initially placed particular care on the 3D surveying accuracy obtainable from such tools. The incorporation of RGB sensors into these instruments has progressively expanded LiDAR-based applications from essential topographic surveying to geospatial applications, where the emphasis is no longer on the accurate three-dimensional reconstruction of buildings but on the capability to create three-dimensional image-based visualizations, such as virtual tours, which allow the recognition of assets located in every area of the buildings. Although much has been written about obtaining the best possible accuracy for extensive asset surveying of large-scale building complexes using iMMS systems, it is now essential to develop and define suitable procedures for controlling such kinds of surveying, targeted at specific geospatial applications. We especially address the design, field acquisition, quality control, and mass data management techniques that might be used in such complex environments. This work aims to contribute by defining the technical specifications for the implementation of geospatial mapping of vast asset survey activities involving significant building sites utilizing iMMS instrumentation. Three-dimensional models can also facilitate virtual tours, enable local measurements inside rooms, and particularly support the subsequent integration of self-locating image-based technologies that can efficiently perform field updates of surveyed databases. Full article

This study introduces an innovative and scalable approach for automated road surface assessment by integrating Mobile Mapping System (MMS)-based LiDAR data analysis with an open-source WebGIS platform. In a U.S.-based case study, over 20 datasets were collected along Interstate I-65 in West Lafayette, Indiana, using the Purdue Wheel-based Mobile Mapping System—Ultra High Accuracy (PWMMS-UHA), following Indiana Department of Transportation (INDOT) guidelines. Preprocessing included noise removal, resolution reduction to 2 cm, and ground/non-ground separation using the Cloth Simulation Filter (CSF), resulting in Bare Earth (BE), Digital Terrain Model (DTM), and Above Ground (AG) point clouds. The optimized BE layer, enriched with intensity and color information, enabled crack detection through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Forest (RF) classification, with and without intensity normalization. DBSCAN parameter tuning was guided by silhouette scores, while model performance was evaluated using precision, recall, F1-score, and the Jaccard Index, benchmarked against reference data. Results demonstrate that RF consistently outperformed DBSCAN, particularly under intensity normalization, achieving Jaccard Index values of 94% for longitudinal and 88% for transverse cracks. A key contribution of this work is the integration of geospatial analytics into an interactive, open-source WebGIS environment—developed using Blender, QGIS, and Lizmap—to support predictive maintenance planning. Moreover, intervention thresholds were defined based on crack surface area, aligned with the Pavement Condition Index (PCI) and FHWA standards, offering a data-driven framework for infrastructure monitoring. This study emphasizes the practical advantages of comparing clustering and machine learning techniques on 3D LiDAR point clouds, both with and without intensity normalization, and proposes a replicable, computationally efficient alternative to deep learning methods, which often require extensive training datasets and high computational resources. Full article

Innovative geospatial solutions are necessary to tackle complex environmental challenges. Mobile mapping systems (MMSs) are such key innovations emerging in this effort. MMSs, with a wide range of applications, significantly impact our increasingly developed data collection technologies by enhancing our understanding of the environment, enabling us to create more detailed models of natural resources, and optimizing the way we live on Earth. In this paper, we present and analyze recent advancements in MMS technologies, focusing on computational and modeling aspects, as well as the latest state-of-the-art sensor, hardware, and software developments. Special attention is given to the trends observed over the past decade, supported by a review of foundational literature. Finally, we outline our vision for the future of MMS, offering insights into the potential for further research and the exciting possibilities that lie ahead in this rapidly evolving field of science and technology. 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

The failure accidents of prestressed concrete cylinder pipe (PCCP) seriously affect the economic feasibility of the construction site. The traditional method of needing to stop construction for pipe inspection is time-consuming and laborious. This paper studies the PCCP broken wire identification algorithm based on deep learning. A PCCP wire-breaking test platform was built; the Distributed Fiber Acoustic Sensing Monitoring System (DAS) monitors wire-breakage events in DN4000mm PCCPs buried underground. The collected broken wire signal creates a time-frequency spectrum diagram dataset of the simulated broken wire signal through continuous wavelet transform (CWT). Considering the location of equipment limitations, based on the YOLOv5 algorithm, a lightweight algorithm, YOLOv5-Break is proposed for broken wire monitoring. Firstly, MobileNetV3 is used to replace the YOLOv5 network backbone, and Dynamic Conv is used to replace Conv in C3 to reduce redundant computation and memory access; the coordinate attention mechanism is integrated into the C3 module to make the algorithm pay more attention to location information; at the same time, CIOU is replaced by Focal_EIoU to make the algorithm pay more attention to high-quality samples and balance the uneven problem of complex and easy examples. The YOLOv5-Break algorithm achieves a mAP of 97.72% on the self-built broken wire dataset, outperforming YOLOv8, YOLOv9, and YOLOv10. Notably, YOLOv5-Break reduces the model weight to 7.74 MB, 46.25% smaller than YOLOv5 and significantly lighter than YOLOv8s and YOLOv9s. With a computational cost of 8.3 GFLOPs, YOLOv5-Break is 71.0% and 78.5% more efficient than YOLOv8s and YOLOv9s. It can be seen that the lightweight algorithm YOLOv5-Break proposed in this article simplifies the algorithm without losing accuracy. Moreover, the lightweight algorithm does not require high hardware computing power and can be better arranged in the PCCP broken wire monitoring system. Full article

This study introduces a new framework for combining calibrated mobile mapping system (MMS) data and low-cost unmanned aerial vehicle (UAV) images to generate seamless, high-fidelity 3D urban maps. This approach addresses the limitations of single-source mapping, such as occlusions in aerial top views and insufficient vertical detail in ground-level data, by utilizing the complementary strengths of the two technologies. The proposed approach combines cloth simulation filtering for ground point extraction from MMS data with deep-learning-based segmentation (U²-Net) for feature extraction from UAV images. Street-view MMS images are projected onto a top-down viewpoint using inverse perspective mapping to align diverse datasets, and precise cross-view alignment is achieved using the LightGlue technique. The spatial accuracy of the 3D model was improved by integrating the matched features as ground control points into a structure from the motion pipeline. Validation using data from the campus of Yonsei University and the nearby urban area of Yeonhui-dong yielded notable accuracy gains and a root mean square error of 0.131 m. Geospatial analysis, infrastructure monitoring, and urban planning can benefit from this flexible and scalable method, which enhances 3D urban mapping capabilities. Full article