Abstract
Abstract. Point clouds are a digital representation of physical objects or buildings that exist in real world. There are many sources that a point cloud can come from such as a terrestrial laser scanner (TLS) or an unmanned aerial vehicle (UAV). This paper presents a simple method of integrating point clouds from two (2) data sources; TLS and UAV using simple alignment of rigid body transformation method known as Point Pair Picking (PPP). The point cloud data are the representation of details of a one-story building located in Johor Bahru, Malaysia. The process of aligning two (2) separate clouds into one (1) dataset requires initial processing such as noise removal before the alignment process was started. A laser (LAS) formatted data were formed so that it compatible with the PPP process. As the result, a high dense hybrid cloud-model was produced covering complete details of the building. This shows that integration of point clouds could improve 3D documentation assessment such as Building Information Modelling (BIM) by contributing richer semantic information.
Highlights
Point clouds are a digital representation of physical objects or buildings that exist in real world
Point clouds are generated by powerful lens which capable to record the objects (Saptari, Hendriatiningsih, Bagaskara, & Apriani, 2019) through Light Detection and Ranging system (LiDAR) which usually installed in a geodetic clouds instrument known as terrestrial laser scanner (TLS)
Point clouds can be generated from cheaper (Sharif, Hazumi, & Meli, 2018; Zięba, 2016) and simpler platform compared to TLS but using imaging system known as unmanned aerial vehicle (UAV)-photogrammetry
Summary
Point clouds are a digital representation of physical objects or buildings that exist in real world. Point clouds are generated by powerful lens which capable to record the objects (Saptari, Hendriatiningsih, Bagaskara, & Apriani, 2019) through Light Detection and Ranging system (LiDAR) which usually installed in a geodetic clouds instrument known as terrestrial laser scanner (TLS). Point clouds can be generated from cheaper (Sharif, Hazumi, & Meli, 2018; Zięba, 2016) and simpler platform compared to TLS but using imaging system known as UAV-photogrammetry. Cloud registration is the process of generating hybrid point clouds. There are three (3) types of cloud registration method that are commonly applied in the industry namely, Iterative Closest Points (ICP) (Li, Wang, Wang, & Tao, 2020), Feature Extraction and Matching (FEM) (Liu, Kong, Zhao, Gong, & Han, 2018) and Match Box Bounding Centre (MBBC) (Ahmad Fuad, Yusoff, Ismail, & Majid, 2018)
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