Development of Point Cloud Data-Denoising Technology for Earthwork Sites Using Encoder-Decoder Network

Choi, Yeongjun; Park, Suyeul; Kim, Seok

doi:10.1007/s12205-022-0407-8

Cited by 4 publications

(1 citation statement)

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“…Previous construction automation research has primarily focused on hardware advancements, such as highway road surface crack inspection and sealing equipment [6,7], bricklaying robots [8], autonomous robotic excavation for dry stone construction [9], and machine guidance (MG) and machine control (MC) for excavators [10,11]. However, the rapid development of Building Information Modeling (BIM) and Artificial Intelligence (AI) technologies has resulted in a significant increase in software-centric construction automation research [12][13][14][15][16][17][18][19]. Furthermore, the technological progress of AI-equipped autonomous vehicles and various sensors has paved the way for AI technology applications in construction, particularly object recognition research [20].…”

Section: Introduction 1research Background and Objectivesmentioning

confidence: 99%

3D Point Cloud Dataset of Heavy Construction Equipment

Park,

Kim

2024

Applied Sciences

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Object recognition algorithms and datasets based on point cloud data have been mainly designed for autonomous vehicles. When applied to the construction industry, they face challenges due to the origin of point cloud data from large earthwork sites, resulting in high volumes of data and density. This research prioritized the development of 3D point cloud datasets specifically for heavy construction equipment, including dump trucks, rollers, graders, excavators, and dozers; all of which are extensively used in earthwork sites. The aim was to enhance the efficiency and productivity of machine learning (ML) and deep learning (DL) research that relies on 3D point cloud data in the construction industry. Notably, unlike conventional approaches to acquiring point cloud data using UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles), the datasets for the five types of heavy construction equipment established in this research were generated using 3D-scanned diecast models of heavy construction equipment to create point cloud data.

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