LiDAR-Based Real-Time Detection and Modeling of Power Lines for Unmanned Aerial Vehicles

The effective monitoring and maintenance of power lines are becoming increasingly important due to a global growing dependence on electricity. The costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced by using UAVs with the appropriate sensors. However, this implies developing algorithms to make the power line inspection process reliable and autonomous. In order to overcome the limitations of visual methods in the presence of poor light and noisy backgrounds, we propose to address the problem of power line detection and modeling based on LiDAR. The PL2DM, Power Line LiDAR-based Detection and Modeling, is a novel approach to detect power lines. Its basis is a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. Using a real dataset, the algorithm showed promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.

[1]  D. I. Jones,et al.  Aerial video inspection of overhead power lines , 2001 .

[2]  Jun Zhou,et al.  An Automatic Technique for Power Line Pylon Detection from Point Cloud Data , 2017, 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

[3]  Prabir K. Pal,et al.  Segmentation of point cloud from a 3D LIDAR using range difference between neighbouring beams , 2015, AIR '15.

[4]  M. Himmelsbach,et al.  Real-time object classification in 3D point clouds using point feature histograms , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Yuri Owechko,et al.  On Real-Time LIDAR Data Segmentation and Classification , 2013 .

[6]  Bart Custers,et al.  Drone Technology: Types, Payloads, Applications, Frequency Spectrum Issues and Future Developments , 2016 .

[7]  David E. Smith,et al.  Space Lidar and Applications , 2001 .

[8]  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.

[9]  Martin Buss,et al.  Realtime segmentation of range data using continuous nearest neighbors , 2009, 2009 IEEE International Conference on Robotics and Automation.

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

[11]  Bertrand Douillard,et al.  On the segmentation of 3D LIDAR point clouds , 2011, 2011 IEEE International Conference on Robotics and Automation.

[12]  Qing Xiang.  3D Reconstruction of 138 KV Power-lines from Airborne LiDAR Data , 2014 .

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

[14]  Markus Ax,et al.  UAV Based Laser Measurement for Vegetation Control at High-Voltage Transmission Lines , 2012 .

[15]  André Dias,et al.  PLineD: Vision-based power lines detection for Unmanned Aerial Vehicles , 2017, 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[16]  Jing Liang,et al.  A New Power-Line Extraction Method Based on Airborne LiDAR Point Cloud Data , 2011, 2011 International Symposium on Image and Data Fusion.

[17]  J. Shan,et al.  Urban DEM generation from raw lidar data: A labeling algorithm and its performance , 2005 .

[18]  Peter Axelsson,et al.  Processing of laser scanner data-algorithms and applications , 1999 .

[19]  Angelika Wronkowicz.  Automatic fusion of visible and infrared images taken from different perspectives for diagnostics of power lines , 2016 .

[20]  Akira Kojima,et al.  Segmentation of 3D Lidar Points Using Extruded Surface of Cross Section , 2015, 2015 International Conference on 3D Vision.

[21]  Gunho Sohn,et al.  Wind adaptive modeling of transmission lines using minimum description length , 2017 .

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

[23]  Qi Zhang,et al.  Extraction of power-transmission lines from vehicle-borne lidar data , 2016 .

[24]  Dirk Wollherr,et al.  A clustering method for efficient segmentation of 3D laser data , 2008, 2008 IEEE International Conference on Robotics and Automation.

[25]  Hiroshi MASAHARU,et al.  THREE-DIMENSIONAL CITY MODELING FROM LASER SCANNER DATA BY EXTRACTING BUILDING POLYGONS USING REGION SEGMENTATION METHOD , 2010 .

[26]  Gunho Sohn,et al.  The Way Forward: Advances in Maintaining Right-of-Way of Transmission Lines , 2019 .

[27]  Myung Jin Chung,et al.  Fast point cloud segmentation for an intelligent vehicle using sweeping 2D laser scanners , 2012, 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[28]  Nikolaos Papanikolopoulos,et al.  Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[29]  André Dias,et al.  Collision avoidance for safe structure inspection with multirotor UAV , 2017, 2017 European Conference on Mobile Robots (ECMR).

[30]  Naoya Takeishi,et al.  Recent Developments in Aerial Robotics: A Survey and Prototypes Overview , 2017, ArXiv.

[31]  C. Briese,et al.  Extraction and Modeling of Power Lines from ALS Point Clouds , 2004 .

[32]  Roland Siegwart,et al.  A comparison of line extraction algorithms using 2D range data for indoor mobile robotics , 2007, Auton. Robots.

[33]  Juha Hyyppä,et al.  Remote sensing methods for power line corridor surveys , 2016 .

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

[35]  Paolo Gamba,et al.  MODEL INDEPENDENT OBJECT EXTRACTION FROM DIGITAL SURFACE MODELS , 2000 .

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

[37]  F. Rottensteiner,et al.  Classification of trees and powerlines from medium resolution airborne laserscanner data in urban environments , 2005 .

[38]  Gregory R. Stockton,et al.  Advances in applications for aerial infrared thermography , 2006, SPIE Defense + Commercial Sensing.

[39]  D. Jones.  Power line inspection - a UAV concept , 2005 .

[40]  Shuang Song,et al.  Automatic Clearance Anomaly Detection for Transmission Line Corridors Utilizing UAV-Borne LIDAR Data , 2018, Remote. Sens..

[41]  Michael Himmelsbach,et al.  Fast segmentation of 3D point clouds for ground vehicles , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[42]  Y. Tseng,et al.  LIDAR DATA SEGMENTATION AND CLASSIFICATION BASED ON OCTREE STRUCTURE , 2004 .

[43]  Miao Wang,et al.  Automatic 3D feature extraction from structuralized LIDAR data , 2005 .

[44]  Myung Jin Chung,et al.  Online urban object recognition in point clouds using consecutive point information for urban robotic missions , 2014, Robotics Auton. Syst..

[45]  Roberto Teti,et al.  A roadmap for automated power line inspection. Maintenance and repair. , 2013 .

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

[47]  Uwe Stilla,et al.  SEGMENTATION OF LASER ALTIMETER DATA FOR BUILDING RECONSTRUCTION: DIFFERENT PROCEDURES AND COMPARISON , 2000 .

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

[49]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[50]  S. Ashidate,et al.  Development of a Helicopter-Mounted Eye-Safe Laser Radar System for Distance Measurement between Power Transmission Lines and Nearby Trees , 2001, IEEE Power Engineering Review.

[51]  Roland Siegwart,et al.  Multimodal detection and tracking of pedestrians in urban environments with explicit ground plane extraction , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[52]  D. Burschka,et al.  Motion segmentation and scene classification from 3D LIDAR data , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[53]  Christoph Stiller,et al.  Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[54]  D. Wanik,et al.  Using vegetation management and LiDAR-derived tree height data to improve outage predictions for electric utilities , 2017 .

[55]  Edwin Olson,et al.  Graph-based segmentation for colored 3D laser point clouds , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[56]  Sebastian Thrun,et al.  Probabilistic Terrain Analysis For High-Speed Desert Driving , 2006, Robotics: Science and Systems.

[57]  J. Almeida,et al.  Control-Law for Oil Spill Mitigation with an Autonomous Surface Vehicle , 2018, 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO).

[58]  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.

[59]  hya sree.M,et al.  Lidar Remote Sensing , 2015 .