Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds

Soon-Wook Kwon; Frederic Nicolas Bosche; Changwan Kim; Carl Haas; Katherine Liapi

doi:10.1016/j.autcon.2003.08.007

Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds

Soon-Wook Kwon, Frederic Nicolas Bosche, Changwan Kim, Carl Haas, Katherine Liapi

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Abstract

Techniques to rapidly model local spaces, using 3D range data, can enable implementation of. (1) real-time obstacle avoidance for improved safety, (2) advanced automated equipment control modes, and (3) as-built data acquisition for improved quantity tracking, engineering, and project control systems. The objective of the research reported here was to develop rapid local spatial modeling tools. Algorithms for fitting sparse range point clouds to geometric primitives such as spheres, cylinders, and cuboids have been developed as well as methods for merging primitives into assemblies. Results of experiments are presented and practical usage and limitations are discussed. (C) 2003 Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	67–81
Number of pages	15
Journal	Automation in Construction
Volume	13
Issue number	1
DOIs	https://doi.org/10.1016/j.autcon.2003.08.007
Publication status	Published - Jan 2004

Keywords

sparse range point clouds
3D workspace modeling
fitting and matching objects
merging objects
AUTOMATION

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title = "Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds",

abstract = "Techniques to rapidly model local spaces, using 3D range data, can enable implementation of. (1) real-time obstacle avoidance for improved safety, (2) advanced automated equipment control modes, and (3) as-built data acquisition for improved quantity tracking, engineering, and project control systems. The objective of the research reported here was to develop rapid local spatial modeling tools. Algorithms for fitting sparse range point clouds to geometric primitives such as spheres, cylinders, and cuboids have been developed as well as methods for merging primitives into assemblies. Results of experiments are presented and practical usage and limitations are discussed. (C) 2003 Elsevier B.V. All rights reserved.",

keywords = "sparse range point clouds, 3D workspace modeling, fitting and matching objects, merging objects, AUTOMATION",

author = "Soon-Wook Kwon and Bosche, {Frederic Nicolas} and Changwan Kim and Carl Haas and Katherine Liapi",

year = "2004",

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AU - Bosche, Frederic Nicolas

AU - Kim, Changwan

AU - Haas, Carl

AU - Liapi, Katherine

PY - 2004/1

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N2 - Techniques to rapidly model local spaces, using 3D range data, can enable implementation of. (1) real-time obstacle avoidance for improved safety, (2) advanced automated equipment control modes, and (3) as-built data acquisition for improved quantity tracking, engineering, and project control systems. The objective of the research reported here was to develop rapid local spatial modeling tools. Algorithms for fitting sparse range point clouds to geometric primitives such as spheres, cylinders, and cuboids have been developed as well as methods for merging primitives into assemblies. Results of experiments are presented and practical usage and limitations are discussed. (C) 2003 Elsevier B.V. All rights reserved.

AB - Techniques to rapidly model local spaces, using 3D range data, can enable implementation of. (1) real-time obstacle avoidance for improved safety, (2) advanced automated equipment control modes, and (3) as-built data acquisition for improved quantity tracking, engineering, and project control systems. The objective of the research reported here was to develop rapid local spatial modeling tools. Algorithms for fitting sparse range point clouds to geometric primitives such as spheres, cylinders, and cuboids have been developed as well as methods for merging primitives into assemblies. Results of experiments are presented and practical usage and limitations are discussed. (C) 2003 Elsevier B.V. All rights reserved.

KW - sparse range point clouds

KW - 3D workspace modeling

KW - fitting and matching objects

KW - merging objects

KW - AUTOMATION

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JO - Automation in Construction

JF - Automation in Construction

IS - 1

ER -

Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds

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Keywords

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