External multi-modal imaging sensor calibration for sensor fusion: A review

Multi-modal data fusion has gained popularity due to its diverse applications, leading to an increased demand for external sensor calibration. Despite several proven calibration solutions, they fail to fully satisfy all the evaluation criteria, including accuracy, automation, and robustness. Thus, this review aims to contribute to this growing field by examining recent research on multi-modal imaging sensor calibration and proposing future research directions. The literature review comprehensively explains the various characteristics and conditions of different multi-modal external calibration methods, including traditional motion-based calibration and feature-based calibration. Target-based calibration and targetless calibration are two types of feature-based calibration, which are discussed in detail. Furthermore, the paper highlights systematic calibration as an emerging research direction. Finally, this review concludes crucial factors for evaluating calibration methods and provides a comprehensive discussion on their applications, with the aim of providing valuable insights to guide future research directions. Future research should focus primarily on the capability of online targetless calibration and systematic multi-modal sensor calibration.Ministerio de Ciencia, Innovación y Universidades | Ref. PID2019-108816RB-I0

Comparative study of road and urban object classification based on mobile laser scanners

Recently, the rapid development of new laser technologies has led to the continuous evolution of mobile laser systems, resulting in even greater capabilities for transport infrastructure. However, the market offers numerous MLS systems with varying specifications for global navigation satellite systems (GNSS), inertial measurement units (IMU), and laser scanners, which can result in different accuracies, resolutions, and densities. In this regard, this paper aims to compare two different MLS system, integrated with different GNSS and IMU for mapping in road and urban environments. The study evaluates the performance of these sensors using different classifiers and neighborhood sizes to determine which sensor produces better results. Random forest was found to be the most suitable classifier with an overall accuracy of (91.81% for Optech and 94.38% for Riegl) in road environment and (86.39% for Optech and 84.21% for Riegl) in urban environment. In terms of MLS, Optech achieved the highest accuracy in the road environment, while Riegl obtained the highest accuracy in the urban environment. This study provides valuable insights into the most effective MLS systems and approaches for accurate mapping in road and urban infrastructure.Xunta de Galicia | Ref. ED481B-2019-061Agencia Estatal de Investigación | Ref. PID2019-108816RB-I0

Controller Design for the Rotational Dynamics of a Quadcopter

Researchers have shown their interests in establishing miniature flying robots to be utilized for, both, commercial and research applications. This is due to that fact that there appears to be a huge advancement in miniature actuators and sensors which depend on the MEMS (Micro Electro-Mechanical Systems) NEMS (Nano-Electro Mechanical Systems). This research underlines a detailed mathematical model and controller design for a quadcopter. The nonlinear dynamic model of the quadcopter is derived from the Newton-Euler method and Euler Lagrange method. The motion of a quadcopter can be classified into two subsystems: a rotational subsystem (attitude and heading) and translational subsystem (altitude and x and y motion). The rotational system is fully actuated whereas translational subsystem is under actuated. However, a quadcopter is 6 DOF (Degrees of Freedom) under actuated system. The controller design of a quadcopter is difficult due to its complex and highly nonlinear mathematical model where the state variables are strongly coupled and contain under actuated property. Nonlinear controller such as SMC (Sliding Mode Controller) is used to control altitude, yaw, pitch, and roll angles.Simulation results show that the robustness of the SMC design gives a better way to design a controller with autonomous stability flight with good tracking performance and improved accuracy without any chattering effect. The system states are following the desired trajectory as expected

Santiago urban dataset SUD: Combination of Handheld and Mobile Laser Scanning point clouds

Santiago Urban Dataset SUD is a real dataset that combines Mobile Laser Scanning (MLS) and Handheld Mobile Laser Scanning (HMLS) point clouds. The data is composed by 2 km of streets, sited in Santiago de Compostela (Spain). Point clouds undergo a manual labelling process supported by both heuristic and Deep Learning methods, resulting in the classification of eight specific classes: road, sidewalk, curb, buildings, vehicles, vegetation, poles, and others. Three PointNet++ models were trained; the first one using MLS point clouds, the second one with HMLS point clouds and the third one with both H&MLS point clouds. In order to ascertain the quality and efficacy of each Deep Learning model, various metrics were employed, including confusion matrices, precision, recall, F1-score, and IoU. The results are consistent with other state-of-the-art works and indicate that SUD is valid for comparing point cloud semantic segmentation works. Furthermore, the survey's extensive coverage and the limited occlusions indicate the potential utility of SUD in urban mobility research.Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Universidade de Vigo/CISU

Obra civil, operacións e mantemento de infraestruturas urbanas

Intelligent Infrastructure is based on digitalization and Big Data solutions for smart mobility. The key factor for smart infrastructures is the predictive maintenance based on connected and digitalized infrastructures. Recently, the concept of building information modelling (BIM) has gained a lot of attention in civil engineering and asset management industries. One of the main challenges faced in Scan to BIM is lack of interoperability. IFC, the final standard, is still limited to major transportation structures (tunnels, rail, roads, and bridges). Therefore, information exchange and standard management protocols is important for the successful implementation of BIM in transport infrastructure. The main aim of this project is to make to make compatible 3D LiDAR point clouds of different nature independently of the capture device used. Automatic compressed data processing and data mining techniques will be developed for the monitoring and extraction of relevant semantic information existing in the transport infrastructure, which will be used as base to build BIM for infrastructure. In the end of the project, the automatic IFC for infrastructure model should allow to analyze the performance, carry out a predictive analysis, test scenarios and review the planned maintenance of the linear structures. So, this should arise as key component of any Intelligent Infrastructure systems mainly for Smart Mobility and Smart LogisticsLas infraestructras inteligentes del transporte se basan en la digitalización y el empleo de Big Data para avanzar en la movilidad inteligente. El factor clave para las infraestructuras inteligentes es el mantenimiento predictivo basado en infraestructuras conectadas y digitalizadas. Recientemente, el concepto de modelado de información de edificios (BIM) ha ganado mucha atención en las industrias de ingeniería civil y gestión de activos. Uno de los principales desafíos a los que se enfrenta “Scan to BIM” es la falta de interoperabilidad. IFC, el estándar final, está aún limitado a las principales estructuras de transporte (túneles, ferrocarriles, carreteras y puentes). Por lo tanto, el intercambio de información y los protocolos de gestión estándar resultan un factor clave para la implementación exitosa de los BIM en las infraestructuras de transporte. El objetivo principal de este proyecto es avanzar en la compatibilidad entre nubes de puntos 3D LiDAR independientemente del dispositivo de captura utilizado. Se desarrollarán técnicas de procesamiento mediante compresión automática y minería de datos para el seguimiento y extracción de información semántica relevante existente en la infraestructura de transporte, que se utilizará como base para construir el BIM de la infraestructura. Al final del proyecto, la IFC automática para el modelo de infraestructura permitirá analizar su rendimiento, realizar un análisis predictivo, probar escenarios y revisar el mantenimiento de las estructuras lineales. Como consecuencia, se traducirá en un aspecto clave en la gestión de cualquier infraestructura inteligente y su orientación a la movilidad y la logística inteligentes.As infraestruturas intelixentes do transporte baséanse na dixitalización e no uso do Big Data para avanzar na mobilidade intelixente. O factor clave para as infraestruturas intelixentes é o mantemento predictivo baseado en infraestruturas conectadas e dixitalizadas. Recentemente, o concepto de Modelado de Información de Edificios (BIM) gañou moita atención nas industrias de enxeñería civil e xestión de activos. Un dos principais retos aos que se enfronta "Scan a BIM" é a falta de interoperabilidade. O IFC, o estándar final, aínda está limitado ás principais estruturas de transporte (túneles, ferrocarrís, estradas e pontes). Polo tanto, o intercambio de información e os protocolos de xestión estándar son un factor clave para a implementación exitosa de BIM en infraestruturas de transporte. O principal obxectivo deste proxecto é avanzar na compatibilidade entre nubes de puntos LiDAR 3D independentemente do dispositivo de captura empregado. As técnicas de procesamento desenvolveranse mediante a compresión automática e a minería de datos para o seguimento e extracción de información semántica relevante existente na infraestrutura de transporte, que se utilizará como base para a construción do BIM da infraestrutura. Ao final do proxecto, o IFC automático para o modelo de infraestrutura permitirá analizar o seu rendemento, realizar análises predictivas, probar escenarios e revisar o mantemento das estruturas lineais. Como consecuencia, converterase nun aspecto clave na xestión de calquera infraestrutura intelixente e na súa orientación cara a mobilidade e loxística intelixentes

Scanning technologies to building information modelling: a review

Building information modelling (BIM) is evolving significantly in the architecture, engineering and construction industries. BIM involves various remote-sensing tools, procedures and standards that are useful for collating the semantic information required to produce 3D models. This is thanks to LiDAR technology, which has become one of the key elements in BIM, useful to capture a semantically rich geometric representation of 3D models in terms of 3D point clouds. This review paper explains the ‘Scan to BIM’ methodology in detail. The paper starts by summarising the 3D point clouds of LiDAR and photogrammetry. LiDAR systems based on different platforms, such as mobile, terrestrial, spaceborne and airborne, are outlined and compared. In addition, the importance of integrating multisource data is briefly discussed. Various methodologies involved in point-cloud processing such as sampling, registration and semantic segmentation are explained in detail. Furthermore, different open BIM standards are summarised and compared. Finally, current limitations and future directions are highlighted to provide useful solutions for efficient BIM models.European Union's Horizon 2020 research and innovation programme | Ref. 860370Ministerio de Ciencia e Innovación | Ref. PID2019-108816RB-I0

Comparative Evaluation of LiDAR systems for transport infrastructure: case studies and performance analysis

ABSTRACTMobile laser scanners are vital for intelligent transport infrastructure, capturing detailed 3D road representations, but their accuracy depends on factors like sensor positioning and environment. This study compares two van-mounted Mobile Laser Scanners (MLS): the dual head Lynx Mobile Mapper and the single head VUX-1 HA, along with the terrestrial laser scanner Faro Focus XX30. Using point cloud reference data from Faro Focus XX30 and GNSS data from Trimble R8, performance is assessed in road, urban, and semi-urban environments. Accuracy is measured by the difference between Trimble GNSS and MLS coordinates. Geometric features of each LiDAR are compared, and mapping tasks in road and urban areas are performed using a machine learning classifier. Results show the MLS-single head scanner achieves satisfactory accuracy in roads and semi-urban areas, while Faro performs better in urban settings for classification. MLS-single head excels in road environments, while Faro is superior in urban ones. This analysis aids researchers and professionals in selecting the appropriate mobile laser scanner for mapping transport infrastructure, providing valuable insights into MLS systems’ comparative performance across different environments