Luminance-Corrected 3D Point Clouds for Road and Street Environments

A novel approach to evaluating night-time road and street environment lighting conditions through 3D point clouds is presented. The combination of luminance imaging and 3D point cloud acquired with a terrestrial laser scanner was used for analyzing 3D luminance on the road surface. A calculation of the luminance (cd/m2) was based on the RGB output values of a Nikon D800E digital still camera. The camera was calibrated with a reference luminance source. The relative orientation between the luminance images and intensity image of the 3D point cloud was solved in order to integrate the data sets into the same coordinate system. As a result, the 3D model of road environment luminance is illustrated and the ability to exploit the method for evaluating the luminance distribution on the road surface is presented. Furthermore, the limitations and future prospects of the methodology are addressed. The method provides promising results for studying road lighting conditions in future lighting optimizations. The paper presents the methodology and its experimental application on a road section which consists of five luminaires installed on one side of a two-lane road in Otaniemi, Espoo, Finland.

[1]  S. J. Oude Elberink,et al.  RAIL TRACK DETECTION AND MODELLING IN MOBILE LASER SCANNER DATA , 2013 .

[2]  Marjukka Puolakka,et al.  Recommended system for mesopic photometry based on visual performance , 2010 .

[3]  Diego González-Aguilera,et al.  Novel approach to 3D thermography and energy efficiency evaluation , 2012 .

[4]  Hongyi Cai,et al.  Measuring Light and Geometry Data of Roadway Environments with a Camera , 2014 .

[5]  Lars Linsen,et al.  An Interactive Visualization System for Huge Architectural Laser Scans , 2015, GRAPP.

[6]  D. C. Brown,et al.  Lens distortion for close-range photogrammetry , 1986 .

[7]  W. Palmer.  Case history. , 1953, McGill medical journal.

[8]  Osram China.  Interpretation & Analysis to EU Commission Regulation(EC)No 245/2009 on Ecodesign Requirements for Fluorescent Lamps without Integrated Ballast,for High Intensity Discharge Lamps,and for Ballasts and Luminaires Able to Operate Such Lamps , 2009 .

[9]  Christopher Edwards,et al.  Development and validation of a luminance camera: final report , 2009 .

[10]  Heikki Summala,et al.  Combined eye-tracking and luminance measurements while driving on a rural road: Towards determining mesopic adaptation luminance , 2014 .

[11]  John A. Kuprenas,et al.  Performance Requirements , 1999 .

[12]  Önder Güler,et al.  The effect of luminance uniformity on visibility level in road lighting , 2003 .

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

[14]  Hannu Hyyppä,et al.  Customized Visualizations of Urban Infill Development Scenarios for Local Stakeholders , 2015 .

[15]  Martin Rutzinger,et al.  Extraction of Vertical Walls from Mobile Laser Scanning Data for Solar Potential Assessment , 2011, Remote. Sens..

[16]  Yuwei Chen,et al.  3D city model for mobile phone using MMS data , 2009, 2009 Joint Urban Remote Sensing Event.

[17]  Pedro Arias,et al.  Automatic detection of road tunnel luminaires using a mobile LiDAR system , 2014 .

[18]  Juha Hyyppä,et al.  Retrieval Algorithms for Road Surface Modelling Using Laser-Based Mobile Mapping , 2008, Sensors.

[19]  Ramon Alamús,et al.  DMC Geometry analysis and virtual image characterisation , 2008 .

[20]  Huaguo Zhou,et al.  A new roadway lighting measurement system , 2009 .

[21]  Qingquan Li,et al.  Automated extraction of street-scene objects from mobile lidar point clouds , 2012 .

[22]  Miomir Kostic,et al.  Recommendations for energy efficient and visually acceptable street lighting , 2009 .

[23]  Jianping Wu,et al.  A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data , 2013, Remote. Sens..

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

[25]  Ryosuke Shibasaki,et al.  AUTO-EXTRACTION OF URBAN FEATURES FROM VEHICLE-BORNE LASER DATA , 2002 .

[26]  Pedro Arias,et al.  Multidisciplinary approach to the assessment of historic structures based on the case of a masonry bridge in Galicia (Spain) , 2011 .

[27]  Elisabetta Rosina,et al.  Mapping Infrared Data on Terrestrial Laser Scanning 3D Models of Buildings , 2011, Remote. Sens..

[28]  P. Litkey,et al.  INTEGRATION OF LASER SCANNING AND PHOTOGRAMMETRY , 2007 .

[29]  Xin Zhang,et al.  Road lighting and headlights: Luminance measurements and automobile lighting simulations , 2008 .

[30]  Dietmar Wüller,et al.  The usage of digital cameras as luminance meters , 2007, Electronic Imaging.

[31]  Peter D. Hiscocks,et al.  Measuring Luminance with a Digital Camera , 2011 .

[32]  Derek D. Lichti,et al.  Accuracy assessment of the FARO Focus 3D and Leica HDS6100 panoramic- type terrestrial laser scanners through point-based and plane-based user self-calibration , 2012 .

[33]  Juha Hyyppä,et al.  Benchmarking the Performance of Mobile Laser Scanning Systems Using a Permanent Test Field , 2012, Sensors.

[34]  Miomir Kostic,et al.  Technical and economic analysis of road lighting solutions based on mesopic vision , 2009 .