Pole-Like Road Furniture Detection In Sparse And Unevenly Distributed Mobile Laser Scanning Data

Pole-like road furniture detection received much attention due to its traffic functionality in recent years. In this paper, we develop a framework to detect pole-like road furniture from sparse mobile laser scanning data. The framework is carried out in four steps. The unorganised point cloud is first partitioned. Then above ground points are clustered and roughly classified after removing ground points. A slicing check in combination with cylinder masking is proposed to extract pole-like road furniture candidates. Pole-like road furniture are obtained after occlusion analysis in the last stage. The average completeness and correctness of pole-like road furniture in sparse and unevenly distributed mobile laser scanning data was above 0.83. It is comparable to the state of art in the field of pole-like road furniture detection in mobile laser scanning data of good quality and is potentially of practical use in the processing of point clouds collected by autonomous driving platforms.

[1]  Claus Brenner,et al.  Extraction of Features from Mobile Laser Scanning Data for Future Driver Assistance Systems , 2009, AGILE Conf..

[2]  Juha Hyyppä,et al.  ROAD ENVIRONMENT MAPPING SYSTEM OF THE FINNISH GEODETIC INSTITUTE - FGI ROAMER - , 2007 .

[3]  Jing Huang,et al.  Pole-like object detection and classification from urban point clouds , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Vladimir G. Kim,et al.  Shape-based recognition of 3D point clouds in urban environments , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[5]  G. Sithole,et al.  Recognising structure in laser scanning point clouds , 2004 .

[6]  S. J. Oude Elberink,et al.  POLE-LIKE STREET FURNITURE DECOMPOSTION IN MOBILE LASER SCANNING DATA , 2016 .

[7]  S. J. Oude Elberink,et al.  Optimizing detection of road furniture (pole-like object) in Mobile Laser Scanner data , 2013 .

[8]  Konrad Schindler,et al.  FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY , 2016 .

[9]  G. Vosselman,et al.  AUTOMATIC EXTRACTION OF VERTICAL WALLS FROM MOBILE AND AIRBORNE LASER SCANNING DATA , 2009 .

[10]  Carlos Cabo,et al.  An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds , 2014 .

[11]  Pedro Arias,et al.  Review of mobile mapping and surveying technologies , 2013 .

[12]  Jouko Lampinen,et al.  Object Classification and Recognition From Mobile Laser Scanning Point Clouds in a Road Environment , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[13]  George Vosselman,et al.  Recognizing basic structures from mobile laser scanning data for road inventory studies , 2011 .

[14]  Juha Hyyppä,et al.  Detection of Vertical Pole-Like Objects in a Road Environment Using Vehicle-Based Laser Scanning Data , 2010, Remote. Sens..

[15]  Bisheng Yang,et al.  Hierarchical extraction of urban objects from mobile laser scanning data , 2015 .

[16]  Sherif Ibrahim El-Halawany,et al.  Detection of Road Poles from Mobile Terrestrial Laser Scanner Point Cloud , 2011, 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping.

[17]  V. Wichmann,et al.  Eigenvalue and graph-based object extraction from mobile laser scanning point clouds , 2013 .

[18]  Bruno Vallet,et al.  TerraMobilita/iQmulus urban point cloud analysis benchmark , 2015, Comput. Graph..

[19]  Arun Kumar Pratihast,et al.  Detection and modelling of 3D trees from mobile laser scanning data , 2010 .

[20]  Yuwei Chen,et al.  Multiplatform Mobile Laser Scanning: Usability and Performance , 2012, Sensors.

[21]  H. Yokoyama,et al.  Detection and Classification of Pole-like Objects from Mobile Laser Scanning Data of Urban Environments , 2013 .

[22]  Jun Yu,et al.  Semiautomated Extraction of Street Light Poles From Mobile LiDAR Point-Clouds , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Roderik Lindenbergh,et al.  SigVox – A 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds , 2017 .

[24]  Bahman Soheilian,et al.  EXTRACTION OF VERTICAL POSTS IN 3D LASER POINT CLOUDS ACQUIRED IN DENSE URBAN AREAS BY A MOBILE MAPPING SYSTEM , 2010 .

[25]  Stefan Hinz,et al.  Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers , 2015 .

[26]  Claus Brenner,et al.  Quality assessment of automatically generated feature maps for future driver assistance systems , 2009, GIS.

[27]  Ahmad Kamal Aijazi,et al.  Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation , 2013, Remote. Sens..

[28]  Hiroshi Masuda,et al.  DETECTION AND CLASSIFICATION OF POLE-LIKE OBJECTS FROM MOBILE MAPPING DATA , 2015 .