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.
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.
Abstract. 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.
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