An automated method for registering lidar data in restrictive, tunnel-like environments

Walter D. Zacherl; Eustace Dereniak; Lars R Furenlid; Eric W Clarkson

doi:10.1117/12.2223453

An automated method for registering lidar data in restrictive, tunnel-like environments

Walter D. Zacherl, Eustace Dereniak, Lars R Furenlid, Eric W Clarkson

Medical Imaging

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A method for automated registration of lidar datasets specifically tailored to geometries with high length-to-width ratios operates on data in curvilinear coordinates. It relaxes the minimum change in perspective requirement between neighboring datasets typical of other algorithms. Range data is filtered with a series of discrete Gaussian and derivative of Gaussian filters to form a second-order Taylor series approximation to the surface about each sampled point. Principal curvatures with respect to the surface normal are calculated and compared across neighboring datasets to determine homologies and the best fit transfer matrix. The method reduces raw data volume requirements and processing time.

Original language	English (US)
Title of host publication	Laser Radar Technology and Applications XXI
Publisher	SPIE
Volume	9832
ISBN (Electronic)	9781510600737
DOIs	https://doi.org/10.1117/12.2223453
State	Published - 2016
Event	Laser Radar Technology and Applications XXI - Baltimore, United States Duration: Apr 19 2016 → Apr 20 2016

Other

Other	Laser Radar Technology and Applications XXI
Country	United States
City	Baltimore
Period	4/19/16 → 4/20/16

Keywords

3D registration
invariant feature
laser radar
scale invariance
spherical coordinate
surface matching
tunnel

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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Zacherl, W. D., Dereniak, E., Furenlid, L. R., & Clarkson, E. W. (2016). An automated method for registering lidar data in restrictive, tunnel-like environments. In Laser Radar Technology and Applications XXI (Vol. 9832). [98320D] SPIE. https://doi.org/10.1117/12.2223453

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title = "An automated method for registering lidar data in restrictive, tunnel-like environments",

abstract = "A method for automated registration of lidar datasets specifically tailored to geometries with high length-to-width ratios operates on data in curvilinear coordinates. It relaxes the minimum change in perspective requirement between neighboring datasets typical of other algorithms. Range data is filtered with a series of discrete Gaussian and derivative of Gaussian filters to form a second-order Taylor series approximation to the surface about each sampled point. Principal curvatures with respect to the surface normal are calculated and compared across neighboring datasets to determine homologies and the best fit transfer matrix. The method reduces raw data volume requirements and processing time.",

keywords = "3D registration, invariant feature, laser radar, scale invariance, spherical coordinate, surface matching, tunnel",

author = "Zacherl, {Walter D.} and Eustace Dereniak and Furenlid, {Lars R} and Clarkson, {Eric W}",

year = "2016",

doi = "10.1117/12.2223453",

language = "English (US)",

volume = "9832",

booktitle = "Laser Radar Technology and Applications XXI",

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note = "Laser Radar Technology and Applications XXI ; Conference date: 19-04-2016 Through 20-04-2016",

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AU - Clarkson, Eric W

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AB - A method for automated registration of lidar datasets specifically tailored to geometries with high length-to-width ratios operates on data in curvilinear coordinates. It relaxes the minimum change in perspective requirement between neighboring datasets typical of other algorithms. Range data is filtered with a series of discrete Gaussian and derivative of Gaussian filters to form a second-order Taylor series approximation to the surface about each sampled point. Principal curvatures with respect to the surface normal are calculated and compared across neighboring datasets to determine homologies and the best fit transfer matrix. The method reduces raw data volume requirements and processing time.

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KW - invariant feature

KW - laser radar

KW - scale invariance

KW - spherical coordinate

KW - surface matching

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