- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Automated and efficient powerline extraction from laser scanning data using a voxel-based subsampling with hierarchical approach
摘要： For periodic monitoring of power utilities, there has been keen interest by utility companies to extract the powerlines from laser scanning data. However, challenges arise when utilizing large point clouds as well as avoiding false positives or other errors in the extraction due to noise from objects in close proximity to the powerlines. In this study, we propose an efficient and robust approach to overcome these challenges through two main steps: candidate powerline point extraction and refinement. In the candidate powerline point extraction step, a voxel-based subsampling structure temporarily substitutes the original scan points with regularly spaced subsampled points that still preserve key details present within the point cloud but significantly reduce the dataset size. After removing the ground surface and adjacent objects, candidate powerline points are efficiently extracted through a hierarchical, feature-based filtering process. In the refinement step, the link between the subsampled candidate powerline points and original scan point cloud enable the original points to be segmented and grouped into clusters. By fitting mathematical models, an individual powerline is re-clustered and used to reconstruct the broken sections in the powerlines. The proposed approach is evaluated on 30 unique datasets with different powerline configurations acquired at five different sites by either a terrestrial or mobile laser scanning system. The parameters are optimized through a sensitivity analysis with pointwise comparison between the extracted powerlines and ground truth using 10 diverse datasets, demonstrating that only one requisite parameter varied as a function of resolution while the remaining parameters were generally consistent across the datasets. With optimized parameters, the proposed approach achieved F1 scores of 88.87–95.47% with high efficiency ranging from 0.81 and 1.46 million points/sec when tested on 30 datasets.
关键词： Lidar,Powerlines,Voxel-based subsampling,Laser scanning,Point cloud
更新于2025-09-23 15:21:01
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[IEEE 2018 17th International Conference on Ground Penetrating Radar (GPR) - Rapperswil, Switzerland (2018.6.18-2018.6.21)] 2018 17th International Conference on Ground Penetrating Radar (GPR) - An FPGA-based Flexible and MIMO-capable GPR System
摘要： Ground penetrating radar (GPR) has broad applications in non-destructive subsurface imaging. Most GPRs on the market are bistatic devices that illuminate the buried objects using analog pulses with simple Gaussian-like shapes. These GPRs suffer from drift in the scan results and have either a low-resolution or a low depth of scan, which limits their application. High resolution along with an increased depth of scan can be achieved by transmitting maximal length pseudorandom sequences (m-sequences) which enable pulse compression due to their near-ideal autocorrelation properties. In addition, improved object localization and reduced drift can be obtained with the spatial diversity offered by a MIMO transceiver. This paper discusses the design and implementation of a 8×8 MIMO-capable impulse-based GPR that transmits m-sequences generated on a low-cost FPGA platform, performs a quadrature transform on the received signal to reduce computation, and implements sub-sampling to sample the quadrature-converted signals using low-speed ADCs. Preliminary experimental results are also presented.
关键词： multistatic,MIMO,pulse compression,subsampling,m-sequence,GPR,Ground penetrating radar
更新于2025-09-09 09:28:46