Real-Time 3D Reconstruction on Construction Site Using Visual SLAM and UAV

Abstract

3D reconstruction can be used as a platform to monitor the performance of activities on construction site, such as construction progress monitoring, structure inspection and post-disaster rescue. Comparing to other sensors, RGB image has the advantages of low-cost, texture rich and easy to implement that has been used as the primary method for 3D reconstruction in construction industry. However, the imagebased 3D reconstruction always requires extended time to acquire and/or to process the image data, which limits its application on time critical projects. Recent progress in Visual Simultaneous Localization and Mapping (SLAM) make it possible to reconstruct a 3D map of construction site in real-time. Integrated with Unmanned Aerial Vehicle (UAV), the obstacles areas that are inaccessible for the ground equipment can also be sensed. Despite these advantages of visual SLAM and UAV, until now, such technique has not been fully investigated on construction site. Therefore, the objective of this research is to present a pilot study of using visual SLAM and UAV for real-time construction site reconstruction. The system architecture and the experimental setup are introduced, and the preliminary results and the potential applications using Visual SLAM and UAV on construction site are discussed.

Development of Modular Bio-Inspired Autonomous Underwater Vehicle for Close Subsea Asset Inspection

“…While visual simultaneous localisation and mapping (SLAM) has seen impressive development for autonomous ground vehicles (AGVs) [31], unmanned aerial vehicles (UAVs) [32] and unmanned underwater vehicles [33], the technical challenges presented by underwater environments have hindered progress for AUVs, particularly in real-time applications. Many unique visual phenomena affect underwater images such as wavelengthdependent attenuation, floating particles and bubbles, underwater caustics in shallow water, varying lights and shadows, moving flora and fauna and refractions through thick glass housing needed for waterproofing camera systems [34,35], some examples of which are shown in Figure 12.…”

Building a Next Generation AI Platform for AEC: A Review and Research Challenges

“…Manual-based data acquisition methods are time-consuming and costly. Unmanned aerial vehicles (UAVs) as a new technology can collect image data in the inaccessible areas or undertake tasks that are dangerous to human beings (Shang and Shen 2017), which is more efficient and cost-effective. More importantly, the images collected via UAVs can provide sufficient visual coverage overlaps of the site to support the generation of 3D building models, which is critical for automated progress monitoring.…”

“…For instance, according to Hamledari et al (2018), swarm intelligence was employed in making the UAVs inspection plan, which reduces the flight duration and thus improves the efficiency of site inspection. Besides, some studies integrated 4D BIM with UAVsenabled 3D reconstruction to achieve autonomous path planning of UAVs (Shang and Shen 2017). Using 4D BIM as a prior model, together with ray-tracing to detect visible elements, the optimal flight missions can be created at locations where have expected changes to optimize the visual coverage of the flight plan (Ibrahim and Golparvar-Fard 2019).…”

Computing in the air: An open airborne computing platform

“…Unlike the aforementioned computing devices, more powerful single‐board computers have not been fully investigated for UAS. Recently, Shang and Shen [32] investigated the computing power of NVIDIA Jetson TX1 [33] with 4 CPU cores, 256 Maxwell CUDA GPU cores, and 4GB memory. Their studies show that NVIDIA Jetson TX1 is still not sufficient enough to achieve real‐time 3D reconstruction and mapping using the simultaneous localisation and mapping algorithm.…”