Power line mapping technique using all-terrain mobile laser scanning

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Power line mapping technique using all-terrain mobile laser scanning. / Lehtomäki, Matti; Kukko, Antero; Matikainen, Leena; Hyyppä, Juha; Kaartinen, Harri; Jaakkola, Anttoni.

julkaisussa: Automation in Construction, Vuosikerta 105, 102802, 01.09.2019.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Lehtomäki, Matti ; Kukko, Antero ; Matikainen, Leena ; Hyyppä, Juha ; Kaartinen, Harri ; Jaakkola, Anttoni. / Power line mapping technique using all-terrain mobile laser scanning. Julkaisussa: Automation in Construction. 2019 ; Vuosikerta 105.

Bibtex - Lataa

@article{08a5bc9fdd0e4dfea354bd92a01e24f7,
title = "Power line mapping technique using all-terrain mobile laser scanning",
abstract = "Power line mapping using remote sensing can automate the traditionally labor-intensive power line corridor inspection. Land-based mobile laser scanning (MLS) can be a good choice for the power line mapping if an aerial inspection is impossible, too costly or slow, unsafe, prohibited by regulations, or if more detailed information on the power line corridor is needed. The mapping of the power lines using MLS was studied in a rural environment outside the road network for the first time. An automatic power line extraction algorithm was developed. The algorithm first found power line candidate points based on the shape and orientation of the local neighborhood of a point using principal component analysis. Power lines were retrieved from the candidates using random sample consensus (Ransac) and a new power line labeling method, which takes into account the three-dimensional shape of the power lines. The new labeling method was able to find the power lines and remove false detections, which were found, for example, from the forest. The algorithm was tested in forested and open field (arable land) areas, outside the road environment using two different platforms of MLS, namely, personal backpack and all-terrain vehicle. The recall and precision of the power line extraction were 93.3{\%} and 93.6{\%}, respectively, using 10 cm as a distance criterion for a successful detection. Drifting of the positioning solution of the scanner was the largest error source, being the (contributory) cause for 60–70{\%} of the errors. The platform did not have a significant effect on the power line extraction accuracy. The accuracy was higher in the open field compared to the forest, because the one-dimensional point density along the power line was inhomogeneous and GNSS (global navigation satellite system) signal was weak in the forest. The results suggest that the power lines can be mapped accurately enough for inspection purposes using MLS in a rural environment outside the road network.",
keywords = "All-terrain, Feature extraction, Forest, Inspection, Lidar, Mobile laser scanning, Power line corridor",
author = "Matti Lehtom{\"a}ki and Antero Kukko and Leena Matikainen and Juha Hyypp{\"a} and Harri Kaartinen and Anttoni Jaakkola",
year = "2019",
month = "9",
day = "1",
doi = "10.1016/j.autcon.2019.03.023",
language = "English",
volume = "105",
journal = "Automation in Construction",
issn = "0926-5805",
publisher = "Elsevier",

}

RIS - Lataa

TY - JOUR

T1 - Power line mapping technique using all-terrain mobile laser scanning

AU - Lehtomäki, Matti

AU - Kukko, Antero

AU - Matikainen, Leena

AU - Hyyppä, Juha

AU - Kaartinen, Harri

AU - Jaakkola, Anttoni

PY - 2019/9/1

Y1 - 2019/9/1

N2 - Power line mapping using remote sensing can automate the traditionally labor-intensive power line corridor inspection. Land-based mobile laser scanning (MLS) can be a good choice for the power line mapping if an aerial inspection is impossible, too costly or slow, unsafe, prohibited by regulations, or if more detailed information on the power line corridor is needed. The mapping of the power lines using MLS was studied in a rural environment outside the road network for the first time. An automatic power line extraction algorithm was developed. The algorithm first found power line candidate points based on the shape and orientation of the local neighborhood of a point using principal component analysis. Power lines were retrieved from the candidates using random sample consensus (Ransac) and a new power line labeling method, which takes into account the three-dimensional shape of the power lines. The new labeling method was able to find the power lines and remove false detections, which were found, for example, from the forest. The algorithm was tested in forested and open field (arable land) areas, outside the road environment using two different platforms of MLS, namely, personal backpack and all-terrain vehicle. The recall and precision of the power line extraction were 93.3% and 93.6%, respectively, using 10 cm as a distance criterion for a successful detection. Drifting of the positioning solution of the scanner was the largest error source, being the (contributory) cause for 60–70% of the errors. The platform did not have a significant effect on the power line extraction accuracy. The accuracy was higher in the open field compared to the forest, because the one-dimensional point density along the power line was inhomogeneous and GNSS (global navigation satellite system) signal was weak in the forest. The results suggest that the power lines can be mapped accurately enough for inspection purposes using MLS in a rural environment outside the road network.

AB - Power line mapping using remote sensing can automate the traditionally labor-intensive power line corridor inspection. Land-based mobile laser scanning (MLS) can be a good choice for the power line mapping if an aerial inspection is impossible, too costly or slow, unsafe, prohibited by regulations, or if more detailed information on the power line corridor is needed. The mapping of the power lines using MLS was studied in a rural environment outside the road network for the first time. An automatic power line extraction algorithm was developed. The algorithm first found power line candidate points based on the shape and orientation of the local neighborhood of a point using principal component analysis. Power lines were retrieved from the candidates using random sample consensus (Ransac) and a new power line labeling method, which takes into account the three-dimensional shape of the power lines. The new labeling method was able to find the power lines and remove false detections, which were found, for example, from the forest. The algorithm was tested in forested and open field (arable land) areas, outside the road environment using two different platforms of MLS, namely, personal backpack and all-terrain vehicle. The recall and precision of the power line extraction were 93.3% and 93.6%, respectively, using 10 cm as a distance criterion for a successful detection. Drifting of the positioning solution of the scanner was the largest error source, being the (contributory) cause for 60–70% of the errors. The platform did not have a significant effect on the power line extraction accuracy. The accuracy was higher in the open field compared to the forest, because the one-dimensional point density along the power line was inhomogeneous and GNSS (global navigation satellite system) signal was weak in the forest. The results suggest that the power lines can be mapped accurately enough for inspection purposes using MLS in a rural environment outside the road network.

KW - All-terrain

KW - Feature extraction

KW - Forest

KW - Inspection

KW - Lidar

KW - Mobile laser scanning

KW - Power line corridor

UR - http://www.scopus.com/inward/record.url?scp=85065883229&partnerID=8YFLogxK

U2 - 10.1016/j.autcon.2019.03.023

DO - 10.1016/j.autcon.2019.03.023

M3 - Article

VL - 105

JO - Automation in Construction

JF - Automation in Construction

SN - 0926-5805

M1 - 102802

ER -

ID: 34324367