Data processing for real-time construction site spatial modeling

Gong, Jie; Caldas, Carlos H.

doi:10.1016/j.autcon.2007.09.002

Cited by 63 publications

(22 citation statements)

References 9 publications

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“…The DBSCAN algorithm, as published in [44], is used to perform data clustering of unorganized sets of data points. It can be applied to various sets of data, including point clouds [45]. It groups together points according to their neighborhood and distance.…”

Section: Data Classification Clarificationmentioning

confidence: 99%

Catenary System Detection, Localization and Classification Using Mobile Scanning Data

Pastucha

2016

Remote Sensing

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This paper presents a new method for detecting, locating and classifying overhead contact systems (catenary systems) in point clouds collected by mobile mapping systems (MMS) on rail roads. Contrary to many other application types, railway embankments are highly regulated and standardized. Railway infrastructure geometric relations remain roughly unchanged within established regions and have similarities between them. The newly-developed method exploits both these characteristics, as well as the survey process. There are several steps in this approach. Firstly, it restricts the search for catenaries relative to the distance to registered MMS trajectory, then finds possible support structures according to the density of points above the track. Subsequently, the method verifies the structures' presence and classifies the points with the use of the RANSAC algorithm. It establishes the presence of cantilevers, as well as poles or structural beams, depending on the type of detected support structure. The method also determines the coordinates of the identified object on the ground. Finally, a classification is clarified with the use of a modified DBSCAN algorithm. The design method has been verified with data collected in four surveys where the cumulative length of the route was almost 90 km. Over 97% of support structures were correctly detected, and out of these, over 95% were completely classified.

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Section: Data Classification Clarificationmentioning

confidence: 99%

Catenary System Detection, Localization and Classification Using Mobile Scanning Data

Pastucha

2016

Remote Sensing

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“…3D spatial modeling can help to optimize equipment control, significantly improve safety (Teizer, Allread, Fullerton, & Hinze, 2010), and enhance a remote operator's spatial perception of the workspace (Cho, Wang, Tang & Haas, 2012). However, the rapid processing of tens of thousands bits of range data in real is still an unsolved problem requiring further investigation (Gong & Caldas, 2008). Unstructured work areas like construction sites are difficult to visualize graphically because they involve highly unpredictable activities and change rapidly.…”

Section: Introductionmentioning

confidence: 99%

Automatic Object Recognition and Registration of Dynamic Construction Equipment from a 3D Point Cloud

Gai¹,

Cho²

2013

Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC 2013): Building Th

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This paper introduces a model-based automatic object recognition and registration framework to assist heavy equipment operators in rapidly perceiving 3D working environment at dynamic construction sites. A video camera and a laser scanner were utilized in this study to rapidly recognize and register dynamic target objects in a 3D space by dynamically separating target object's point cloud data from a background scene for a quick computing process. A smart scan data updating method has been developed which only updates the dynamic target object's point cloud data while keeping the previously scanned static work environments. Extracted target areas containing 3D point clouds were orthographically projected into a series of 2D planes with a rotation center located in the target's vertical-middle line. Prepared 2D templates were compared to these 2D planes by extracting SURF features. Point cloud bundles of the target were recognized, and followed by the prepared CAD model's registration to the templates. The field experimental results show that the proposed rapid workspace modeling method can significantly improve heavy construction equipment operations and automated equipment control by rapidly modeling dynamic target objects in a 3D view.

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“…3D spatial modeling can help to optimize equipment control [1,24], significantly improve safety [2][3], monitor construction progress [4], and enhance a remote operator's spatial perception of the workspace [5][6][7][8]. However, the rapid processing of tens of thousand bits of range data in real time is still an unsolved problem requiring further investigation [9]. Unstructured work areas like construction sites are difficult to graphically visualize because they highly involve unpredictable activities and change rapidly.…”

Section: Introductionmentioning

confidence: 99%

Performance Test for Rapid Surface Modeling of DynamicConstruction Equipment from Laser Scanner Data

Wang¹,

Cho²

2014

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-In modern dynamic construction fields, more attention has been paid on safely operating heavy construction equipment such as cranes, excavators, and concrete pump. In order to improve the safety in complicated jobsites, it is highly required to provide heavy equipment operators with accurate measurement of on-site objects in near real time. In this paper, a rapid surface modeling method and its performance evaluation through on-site tests are introduced. The performance of the proposed method was tested with a heavy equipment at construction site. The interrelationships among data size, processing time, and the size of the resultant hull segments were examined from the data analyses.The field experimental results demonstrate that the proposed dynamic surface modeling method would significantly improve the equipment operation productivity and safety by distinguishing a dynamic surface model being controlled by the operator from the point cloud of existing static environment in 3D views.

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Data processing for real-time construction site spatial modeling

Cited by 63 publications

References 9 publications

Catenary System Detection, Localization and Classification Using Mobile Scanning Data

Catenary System Detection, Localization and Classification Using Mobile Scanning Data

Automatic Object Recognition and Registration of Dynamic Construction Equipment from a 3D Point Cloud

Performance Test for Rapid Surface Modeling of DynamicConstruction Equipment from Laser Scanner Data

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