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Title: Multiview marker-free registration of forest terrestrial laser scanner data with embedded confidence metrics

Terrestrial laser scanning has demonstrated increasing potential for rapid comprehensive measurement of forest structure, especially when multiple scans are spatially registered in order to reduce the limitations of occlusion. Although marker-based registration techniques (based on retro-reflective spherical targets) are commonly used in practice, a blind marker-free approach is preferable, insofar as it supports rapid operational data acquisition. To support these efforts, we extend the pairwise registration approach of our earlier work, and develop a graph-theoretical framework to perform blind marker-free global registration of multiple point cloud data sets. Pairwise pose estimates are weighted based on their estimated error, in order to overcome pose conflict while exploiting redundant information and improving precision. The proposed approach was tested for eight diverse New England forest sites, with 25 scans collected at each site. Quantitative assessment was provided via a novel embedded confidence metric, with a mean estimated root-mean-square error of 7.2 cm and 89% of scans connected to the reference node. Lastly, this paper assesses the validity of the embedded multiview registration confidence metric and evaluates the performance of the proposed registration algorithm.
Authors:
 [1] ;  [2] ;  [1] ;  [1] ;  [1] ;  [1]
  1. Rochester Institute of Technology, Rochester, NY (United States)
  2. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Grant/Contract Number:
AC05-00OR22725
Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Geoscience and Remote Sensing
Additional Journal Information:
Journal Volume: 55; Journal Issue: 2; Journal ID: ISSN 0196-2892
Publisher:
IEEE
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Laboratory Directed Research and Development (LDRD) Program
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 47 OTHER INSTRUMENTATION; laser radar; forestry; image registration
OSTI Identifier:
1350937

Kelbe, David, Oak Ridge National Lab., van Aardt, Jan, Romanczyk, Paul, van Leeuwen, Martin, and Cawse-Nicholson, Kerry. Multiview marker-free registration of forest terrestrial laser scanner data with embedded confidence metrics. United States: N. p., Web. doi:10.1109/TGRS.2016.2614251.
Kelbe, David, Oak Ridge National Lab., van Aardt, Jan, Romanczyk, Paul, van Leeuwen, Martin, & Cawse-Nicholson, Kerry. Multiview marker-free registration of forest terrestrial laser scanner data with embedded confidence metrics. United States. doi:10.1109/TGRS.2016.2614251.
Kelbe, David, Oak Ridge National Lab., van Aardt, Jan, Romanczyk, Paul, van Leeuwen, Martin, and Cawse-Nicholson, Kerry. 2016. "Multiview marker-free registration of forest terrestrial laser scanner data with embedded confidence metrics". United States. doi:10.1109/TGRS.2016.2614251. https://www.osti.gov/servlets/purl/1350937.
@article{osti_1350937,
title = {Multiview marker-free registration of forest terrestrial laser scanner data with embedded confidence metrics},
author = {Kelbe, David and Oak Ridge National Lab. and van Aardt, Jan and Romanczyk, Paul and van Leeuwen, Martin and Cawse-Nicholson, Kerry},
abstractNote = {Terrestrial laser scanning has demonstrated increasing potential for rapid comprehensive measurement of forest structure, especially when multiple scans are spatially registered in order to reduce the limitations of occlusion. Although marker-based registration techniques (based on retro-reflective spherical targets) are commonly used in practice, a blind marker-free approach is preferable, insofar as it supports rapid operational data acquisition. To support these efforts, we extend the pairwise registration approach of our earlier work, and develop a graph-theoretical framework to perform blind marker-free global registration of multiple point cloud data sets. Pairwise pose estimates are weighted based on their estimated error, in order to overcome pose conflict while exploiting redundant information and improving precision. The proposed approach was tested for eight diverse New England forest sites, with 25 scans collected at each site. Quantitative assessment was provided via a novel embedded confidence metric, with a mean estimated root-mean-square error of 7.2 cm and 89% of scans connected to the reference node. Lastly, this paper assesses the validity of the embedded multiview registration confidence metric and evaluates the performance of the proposed registration algorithm.},
doi = {10.1109/TGRS.2016.2614251},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
number = 2,
volume = 55,
place = {United States},
year = {2016},
month = {10}
}