A Robust Model Fitting-based Method for Transmission Line Extraction from Airborne LiDAR Point Cloud Data

doi:10.11947/j.JGGS.2021.0306

Abstract

Abstract:

Airborne Light Detection And Ranging (LiDAR) can provide high-quality three-dimensional information for the safety inspection of electricity corridors. However, the robust extraction of transmission lines from airborne point cloud data is still greatly challenging. Therefore, this paper proposes a robust transmission line extraction method based on model fitting from airborne point cloud data. First, the candidate power line generation method based on height information is used to reduce the computational complexity at the subsequent steps and the false positives in the extracted results. Then, on the basis of the block-and-slice-constraint Euclidean clustering, a linear structure recognition method based on RANdom SAmple Consensus (RANSAC) is proposed to produce the initial individual transmission line components. Finally, a robust nonlinear least square-based fitting method is developed for the individual transmission line to generate the parameters of its mathematical model for further optimizing the extraction. Experiments were performed on LiDAR point cloud data captured from the helicopter and Unmanned Aerial Vehicle (UAV) platform. Results indicate that the proposed method can efficiently extract the different types of transmission lines along electricity corridors, with the average precision of approximately 98.1%, the average recall of approximately 95.9%, and the average quality of approximately 94.2%, respectively.

Key words: airborne LiDAR; transmission line extraction; unsupervised method; random sample consensus

Juntao YANG,Zhizhong KANG,Zhou YANG. A Robust Model Fitting-based Method for Transmission Line Extraction from Airborne LiDAR Point Cloud Data[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(3): 60-71.

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Datasets	Powerlines	Others	Total(M)	Point density /(points/m²)	Horizontal clearance/m	Length/m	Terrain	Platform
Area #1	86370 (1.69%)	5016081 (98.31%)	5.10	40	6.24	3169	Complex	Helicopter
Area #2	693627 (2.80%)	24057543 (97.20%)	24.75	40	9.89	1839	Complex	UAV
Area #3	171006 (1.31%)	12916334 (98.69%)	13.08	50	21.89	1898	Flat	Helicopter
Area #4	208460 (2.59%)	7846574 (97.41%)	8.05	100	10.67	3740	Flat	Helicopter

Parameters	Associated values/m	Descriptions
H_threshold	20	The height threshold above the ground
Dis_threshold	1.5	The distance threshold to the fitted line
Block_size	30×30	The size of block
Slice_interval	10	The interval of the adjacent slices
Dis_clustering	4	The distance threshold in Euclidean clustering algorithm
L_threshold	20	The length threshold of the initial individual powerlines

Datasets	Precision/(%)	Recall/(%)	Quality/(%)	Max error_h	RMSE_h	Max error_v	RMSE_v
Area #1	98.1	96.8	95.0	0.711	0.16	0.64	0.06
Area #2	97.3	95.1	92.6	0.65	0.19	0.94	0.32
Area #3	98.9	96.0	95.1	0.90	0.22	1.01	0.23
Area #4	98.2	95.8	94.1	0.67	0.16	0.89	0.17
Average	98.1	95.9	94.2	0.73	0.18	0.87	0.19

Methods	Mode	Platform	Scene	Precision	Recall	Quality
Method in Literature[13]	Supervised	Airborne	Mountains	96.0	92.5	92.5
Method in Literature[16]	Unsupervised	Mobile	Urban	98.1	99.8	—
Method in Literature[19]	Unsupervised	Mobile	Urban	99.0	92.0	92.0
Method in Literature[20]	Supervised	Airborne	Urban	98.8	98.2	97.2
Proposed method	Unsupervised	Airborne	Mountains	98.1	95.9	94.2