Abstract
Abstract. In this paper, we focus on the development of intelligent construction vehicles to improve the safety of workers in construction sites. Generally, global navigation satellite system positioning is utilized to obtain the position data of workers and construction vehicles. However, construction fields in urban areas have poor satellite positioning environments. Therefore, we have developed a 3D sensing unit mounted on a construction vehicle for worker position data acquisition. The unit mainly consists of a multilayer laser scanner. We propose a real-time object measurement, classification and tracking methodology with the multilayer laser scanner. We also propose a methodology to estimate and visualize object behaviors with a spatial model based on a space subdivision framework consisting of agents, activities, resources, and modifiers. We applied the space subdivision framework with a geofencing approach using real-time object classification and tracking results estimated from temporal point clouds. Our methodology was evaluated using temporal point clouds acquired from a construction vehicle in drilling works.
Highlights
The construction field has recently focused on technical and political issues, such as construction management costs, productivity improvement, and reducing the number of accidents (Dong et al 2018)
We propose a methodology for real-time object measurement, classification and tracking from temporal point clouds acquired with a multilayer laser scanner
We propose a methodology to estimate and visualize object behaviors with a spatial model based on a space subdivision framework consisting of agents, activities, resources, and modifiers (Sithole and Zlatanova, 2016)
Summary
The construction field has recently focused on technical and political issues, such as construction management costs, productivity improvement, and reducing the number of accidents (Dong et al 2018). Construction fields in urban areas have poor satellite positioning environments To address these issues, we applied 3D sensing to provide more stable worker position data acquisition and collision-avoidance sensing of construction vehicles to improve incident prediction. With UAVs and terrestrial laser scanners, it is not easy to measure and represent changing objects and environments, such as moving workers, vehicles, and construction fields in real time. We propose a methodology for real-time object measurement, classification and tracking from temporal point clouds acquired with a multilayer laser scanner. We applied the space subdivision framework with a geofencing approach using the results of real-time object classification and tracking from temporal point clouds. We evaluated our methodology using temporal point clouds acquired from a construction vehicle in drilling works
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