UAV-Based Oblique Photogrammetry for Outdoor Data Acquisition and Offsite Visual Inspection of Transmission Line

Regular inspection of transmission lines is an essential work, which has been implemented by either labor intensive or very expensive approaches. 3D reconstruction could be an alternative solution to satisfy the need for accurate and low cost inspection. This paper exploits the use of an unmanned aerial vehicle (UAV) for outdoor data acquisition and conducts accuracy assessment tests to explore potential usage for offsite inspection of transmission lines. Firstly, an oblique photogrammetric system, integrating with a cheap double-camera imaging system, an onboard dual-frequency GNSS (Global Navigation Satellite System) receiver and a ground master GNSS station in fixed position, is designed to acquire images with ground resolutions better than 3 cm. Secondly, an image orientation method, considering oblique imaging geometry of the dual-camera system, is applied to detect enough tie-points to construct stable image connection in both along-track and across-track directions. To achieve the best geo-referencing accuracy and evaluate model measurement precision, signalized ground control points (GCPs) and model key points have been surveyed. Finally, accuracy assessment tests, including absolute orientation precision and relative model precision, have been conducted with different GCP configurations. Experiments show that images captured by the designed photogrammetric system contain enough information of power pylons from different viewpoints. Quantitative assessment demonstrates that, with fewer GCPs for image orientation, the absolute and relative accuracies of image orientation and model measurement are better than 0.3 and 0.2 m, respectively. For regular inspection of transmission lines, the proposed solution can to some extent be an alternative method with competitive accuracy, lower operational complexity and considerable gains in economic cost.

[1]  G. Sohn,et al.  AUTOMATIC 3 D POWERLINE RECONSTRUCTION USING AIRBORNE LiDAR DATA , 2009 .

[2]  Jean Ponce,et al.  Accurate, Dense, and Robust Multiview Stereopsis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Qingquan Li,et al.  An Improved Method for PowerLine Reconstruction from Point Cloud Data , 2016 .

[4]  M. Pierrot Deseilligny,et al.  APERO, AN OPEN SOURCE BUNDLE ADJUSMENT SOFTWARE FOR AUTOMATIC CALIBRATION AND ORIENTATION OF SET OF IMAGES , 2012 .

[5]  Aamir Saeed Malik,et al.  Vegetation encroachment monitoring for transmission lines right-of-ways: A survey , 2013 .

[6]  Wuming Zhang,et al.  An airborne multi-angle power line inspection system , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[7]  Yu Wang,et al.  Extraction of Urban Power Lines from Vehicle-Borne LiDAR Data , 2014, Remote. Sens..

[8]  Ran Wang,et al.  Tridimensional Reconstruction Applied to Cultural Heritage with the Use of Camera-Equipped UAV and Terrestrial Laser Scanner , 2014, Remote. Sens..

[9]  Fabio Remondino,et al.  Aerial multi-camera systems: Accuracy and block triangulation issues , 2015 .

[10]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[11]  J. Katrasnik,et al.  A Survey of Mobile Robots for Distribution Power Line Inspection , 2010, IEEE Transactions on Power Delivery.

[12]  Rongjun Qin,et al.  An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images , 2014, Remote. Sens..

[13]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Qingquan Li,et al.  An Improved Method for Power-Line Reconstruction from Point Cloud Data , 2016, Remote. Sens..

[15]  Qingquan Li,et al.  A Stochastic Geometry Method for Pylon Reconstruction from Airborne LiDAR Data , 2016, Remote. Sens..

[16]  Jinhai Cai,et al.  Evaluation of Aerial Remote Sensing Techniques for Vegetation Management in Power-Line Corridors , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[17]  K. Moffett,et al.  Remote Sens , 2015 .

[18]  Impyeong Lee,et al.  A UAV BASED CLOSE-RANGE RAPID AERIAL MONITORING SYSTEM FOR EMERGENCY RESPONSES , 2012 .

[19]  J. Gonçalves,et al.  UAV photogrammetry for topographic monitoring of coastal areas , 2015 .

[20]  Juha Hyyppä,et al.  Fully-Automated Power Line Extraction from Airborne Laser Scanning Point Clouds in Forest Areas , 2014, Remote. Sens..

[21]  Emmanuel P. Baltsavias,et al.  Airborne laser scanning: existing systems and firms and other resources , 1999 .

[22]  Filiberto Chiabrando,et al.  UAV Deployment Exercise for Mapping Purposes: Evaluation of Emergency Response Applications , 2015, Sensors.

[23]  Changming Sun,et al.  Measuring the distance of vegetation from powerlines using stereo vision , 2006 .

[24]  Jason J. Ford,et al.  Toward automated power line corridor monitoring using advanced aircraft control and multisource feature fusion , 2012, J. Field Robotics.

[25]  Andrea Maria Lingua,et al.  An Image-Based Approach for the Co-Registration of Multi-Temporal UAV Image Datasets , 2016, Remote. Sens..

[26]  A. Bolten,et al.  INTRODUCING A LOW-COST MINI-UAV FOR THERMAL- AND MULTISPECTRAL-IMAGING , 2012 .

[27]  Fabio Remondino,et al.  UAV PHOTOGRAMMETRY FOR MAPPING AND 3D MODELING - CURRENT STATUS AND FUTURE PERSPECTIVES - , 2012 .

[28]  Kai-Wei Chiang,et al.  The Development of an UAV Borne Direct Georeferenced Photogrammetric Platform for Ground Control Point Free Applications , 2012, Sensors.

[29]  Antonia Teresa Spano,et al.  UAV PHOTOGRAMMETRY WITH OBLIQUE IMAGES: FIRST ANALYSIS ON DATA ACQUISITION AND PROCESSING , 2016 .

[30]  A. Mouget,et al.  PHOTOGRAMMETRIC ARCHAEOLOGICAL SURVEY WITH UAV , 2014 .

[31]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[32]  Livio Pinto,et al.  Experimental analysis of different software packages for orientation and digital surface modelling from UAV images , 2014, Earth Science Informatics.

[33]  J.-M Friedt Photogrammetric 3 D structure reconstruction using Micmac , 2014 .

[34]  References , 1971 .

[35]  Marco Dubbini,et al.  Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments , 2013, Remote. Sens..

[36]  Danilo Schneider,et al.  Analysis of Different Methods for 3D Reconstruction of Natural Surfaces from Parallel‐Axes UAV Images , 2015 .

[37]  Steven M. Seitz,et al.  Photo tourism: exploring photo collections in 3D , 2006, ACM Trans. Graph..

[38]  Aamir Saeed Malik,et al.  A novel method for vegetation encroachment monitoring of transmission lines using a single 2D camera , 2014, Pattern Analysis and Applications.

[39]  T. Katayama,et al.  MEASUREMENT OF LARGE-SCALE SOLAR POWER PLANT BY USING IMAGES ACQUIRED BY NON-METRIC DIGITAL CAMERA ON BOARD UAV , 2012 .

[40]  Isabelle CLÉRY,et al.  AN ERGONOMIC INTERFACE TO COMPUTE 3 D MODELS USING PHOTOGRAMMETRY , 2011 .

[41]  K. Bakuła,et al.  TOWARDS EFFICIENCY OF OBLIQUE IMAGES ORIENTATION , 2016 .

[42]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[43]  Arko Lucieer,et al.  Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery , 2012, Remote. Sens..

[44]  A. T. Johns,et al.  An overview of the condition monitoring of overhead lines , 2000 .

[45]  Yoonseok Jwa,et al.  AUTOMATIC 3D POWERLINE RECONSTRUCTION USING AIRBORNE LiDAR DATA , 2009 .

[46]  Pablo J. Zarco-Tejada,et al.  High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials , 2015, Remote. Sens..

[47]  F. Ackermann Airborne laser scanning : present status and future expectations , 1999 .

[48]  Horst Bischof,et al.  TOWARDS FULLY AUTOMATIC PHOTOGRAMMETRIC RECONSTRUCTION USING DIGITAL IMAGES TAKEN FROM UAVS , 2010 .

[49]  Antonia Teresa Spano,et al.  ARCHAEOLOGICAL SITE MONITORING: UAV PHOTOGRAMMETRY CAN BE AN ANSWER , 2012 .

[50]  Robert A. McLaughlin,et al.  Extracting transmission lines from airborne LIDAR data , 2006, IEEE Geoscience and Remote Sensing Letters.