UAV Based Laser Measurement for Vegetation Control at High-Voltage Transmission Lines

Ax, Markus; Thamke, Stefan; Kuhnert, Lars; Kuhnert, Klaus-Dieter

doi:10.4028/www.scientific.net/amr.614-615.1147

Cited by 10 publications

(10 citation statements)

References 5 publications

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“…Lin, Hyyppa, Rosnell, Jaakkola, and Honkavaara (2013) investigated the combined use of data collected from UAV imaging system and mobile mapping system for land cover classification in an urban environment. Owing to the high mobility, repeated surveys can be easily performed under different environments so as to serve ad-hoc request or rapid response, such as ice surface observation (Crocker et al, 2012), vegetation control (Ax, Thamke, Kuhnert, & Kuhnert, 2013), and forestry monitoring (Wallace, Lucieer, Watson, & Turner, 2012). As these initial trials demonstrated promising results toward different applications, the introduction of such portable sensor opens a new door for many research directions, such as system calibration, sensor modeling, data fusion with onboard camera, and on-line data processing.…”

Section: Unmanned Aerial Vehiclementioning

confidence: 99%

Urban land cover classification using airborne LiDAR data: A review

Yan

Shaker

El-Ashmawy

2015

Remote Sensing of Environment

462

270

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Section: Unmanned Aerial Vehiclementioning

confidence: 99%

Urban land cover classification using airborne LiDAR data: A review

Yan

Shaker

El-Ashmawy

2015

Remote Sensing of Environment

462

270

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“…Benefit from its characteristics of long-range detection of objects or natural phenomena without direct contact, remote sensing methods have become one of the important technical means to acquire spatial and spectral information of the power transmission grid which is the foundation of further safety hazard detection and analysis. The applied RS data sources for the inspection of ROWs mainly include: synthetic aperture radar (SAR) images [19,20], optical satellite images [11,21], optical aerial images [22], thermal images [23,24], airborne laser scanning (ALS) data [25][26][27][28], land-based mobile mapping data [29], and unmanned aerial vehicle (UAV) laser scanning data [30,31].…”

Section: Introductionmentioning

confidence: 99%

Automatic Clearance Anomaly Detection for Transmission Line Corridors Utilizing UAV-Borne LIDAR Data

et al. 2018

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Transmission line corridor (i.e., Right-of-Ways (ROW)) clearance management plays a critically important role in power line risk management and is an important task of the routine power line inspection of the grid company. The clearance anomaly detection measures the distance between the power lines and the surrounding non-power-facility objects in the corridor such as trees, and buildings, to judge whether the clearance is within the safe range. To find the clearance hazards efficiently and flexibly, this study thus proposed an automatic clearance anomaly detection method utilizing LiDAR point clouds collected by unmanned aerial vehicle (UAV). Firstly, the terrain points were filtered out using two-step adaptive terrain filter and the pylons were detected in the non-terrain points following a feature map method. After dividing the ROW point clouds into spans based on the pylon detection results, the power line point clouds were extracted according to their geometric distribution in local span point clouds slices, and were further segmented into clusters by applying conditional Euclidean clustering with linear feature constraints. Secondly, the power line point clouds segments were iteratively fitted with 3D catenary curve model that is represented by a horizontal line and a vertical catenary curve defined by a hyperbolic cosine function, resulting in a continuous mathematical model of the discretely sampled points of the power line. Finally, a piecewise clearance calculation method which converts the point-to-catenary curve distance measurements to minimal distance calculation based on differential geometry was used to calculate the distance between the power line and the non-power-facility objects in the ROW. The clearance measurements were compared with the standard safe threshold to find the clearance anomalies in the ROWs. Multiple LiDAR point clouds datasets collected by a large-scale UAV power line inspection system were used to validate the effectiveness and accuracy of the proposed method. The detected results were validated through qualitatively visual inspection, quantitatively manual measurements in raw point clouds and on-site field survey. The experiments show that the automatic clearance anomaly detection method proposed in this paper effectively detects the clearance hazards such as tree encroachment, and the clearance measurement accuracy is decimeter level for the LiDAR point clouds collected by our UAV inspection system.

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“…Recently, compact and lightweight UAV-LiDAR sensors were introduced into the market, although their performance is limited in terms of scan speed and measurement rate [11]. Ax et al in [12] presented the use of laser scanner data from a UAV helicopter for vegetation control at high-voltage transmission lines; Teng et al in [13] proposed their own LiDAR-UAV system. However, most of these systems (LiDAR, SAR, satellite imagery) are quite costly, which is why recent research has focused more on processing UAV imagery data in a way that allows the highest possible accuracy of 3D reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Improvement of 3D Power Line Extraction from Multiple Low-Cost UAV Imagery Using Wavelet Analysis

Fryśkowska

2019

Sensors

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Three-dimensional (3D) mapping of power lines is very important for power line inspection. Many remotely-sensed data products like light detection and ranging (LiDAR) have been already studied for power line surveys. More and more data are being obtained via photogrammetric measurements. This increases the need for the implementation of advanced processing techniques. In recent years, there have been several developments in visualisation techniques using UAV (unmanned aerial vehicle) platform photography. The most modern of such imaging systems have the ability to generate dense point clouds. However, image-based point cloud accuracy is very often various (unstable) and dependent on the radiometric quality of images and the efficiency of image processing algorithms. The main factor influencing the point cloud quality is noise. Such problems usually arise with data obtained via low-cost UAV platforms. Therefore, generated point clouds representing power lines are usually incomplete and noisy. To obtain a complete and accurate 3D model of power lines and towers, it is necessary to develop improved data processing algorithms. The experiment tested the algorithms on power lines with different voltages. This paper presents the wavelet-based method of processing data acquired with a low-cost UAV camera. The proposed, original method involves the application of algorithms for coarse filtration and precise filtering. In addition, a new way of calculating the recommended flight height was proposed. At the end, the accuracy assessment of this two-stage filtration process was examined. For this, point quality indices were proposed. The experimental results show that the proposed algorithm improves the quality of low-cost point clouds. The proposed methods improve the accuracy of determining the parameters of the lines by more than twice. About 10% of noise is reduced by using the wavelet-based approach.

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UAV Based Laser Measurement for Vegetation Control at High-Voltage Transmission Lines

Cited by 10 publications

References 5 publications

Urban land cover classification using airborne LiDAR data: A review

Urban land cover classification using airborne LiDAR data: A review

Automatic Clearance Anomaly Detection for Transmission Line Corridors Utilizing UAV-Borne LIDAR Data

Improvement of 3D Power Line Extraction from Multiple Low-Cost UAV Imagery Using Wavelet Analysis

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