Mapping curbstones in airborne and mobile laser scanning data

Abstract The high point densities obtained by today's laser scanning systems enable the extraction of various features which are traditionally mapped by photogrammetry or land surveying. While significant progress has been made in the extraction of buildings and trees from dense point clouds, little research has been performed on the extraction of roads. In this paper it is analysed to what extent road sides can be mapped in point clouds of high point density. In urban areas curbstones are often used to separate the road surface from the adjacent pavement. These curbstones are mapped in a three step procedure. First, the locations with small height jumps near the terrain surface are detected. Second, midpoints of high and low points on either side of the height jump are generated, put in a sequence to obtain a polygonal chain describing the approximate curbstone location. A sigmoidal function is then fitted to all points near the polygonal chain to increase the accuracy. Third, small gaps between nearby and collinear line segments are closed. GPS measurements were taken to analyse the performance of the road side detection. The analysis showed that the completeness in airborne laser scanning (ALS) data varying between 53% and 92% is higher than that in mobile laser scanning (MLS) data ranging from 54% to 83%, depending on the amount of parked cars occluding the curbstones. The RMS value in the comparison with the GPS points measured from ground survey was 0.11 m in ALS data and 0.06 m in MLS data, respectively.

[1]  F. Rottensteiner,et al.  The Automatic Extraction of Roads from LIDAR data , 2004 .

[2]  E. Denis,et al.  TOWARDS ROAD MODELLING FROM TERRESTRIAL LASER POINTS , 2010 .

[3]  G. Vosselman.  SLOPE BASED FILTERING OF LASER ALTIMETRY DATA , 2000 .

[4]  George Vosselman,et al.  Tree modelling from mobile laser scanning data‐sets , 2011 .

[5]  C. Brenner.  Building reconstruction from images and laser scanning , 2005 .

[6]  Chunhe Yu,et al.  Road Curbs Detection Based on Laser Radar , 2006, 2006 8th international Conference on Signal Processing.

[7]  George Vosselman,et al.  Analysis of planimetric accuracy of airborne laser scanning surveys , 2008 .

[8]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[9]  Peter J. Kootsookos,et al.  Improving city model determination by using road detection from LIDAR data , 2005 .

[10]  Wolfgang Förstner,et al.  Curb reconstruction using Conditional Random Fields , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[11]  Wende Zhang,et al.  LIDAR-based road and road-edge detection , 2010, 2010 IEEE Intelligent Vehicles Symposium.

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

[13]  K. Kraus,et al.  ADVANCED DTM GENERATION FROM LIDAR DATA , 2001 .

[14]  N. Pfeifer,et al.  AUTOMATIC RECONSTRUCTION OF SINGLE TREES FROM TERRESTRIAL LASER SCANNER DATA , 2004 .

[15]  George Vosselman,et al.  Building Reconstruction by Target Based Graph Matching on Incomplete Laser Data: Analysis and Limitations , 2009, Sensors.

[16]  Robert C. Bolles,et al.  A RANSAC-Based Approach to Model Fitting and Its Application to Finding Cylinders in Range Data , 1981, IJCAI.

[17]  George Vosselman,et al.  Knowledge based reconstruction of building models from terrestrial laser scanning data , 2009 .

[18]  G. Vosselman,et al.  The utilisation of airborne laser scanning for mapping , 2005 .

[19]  J. Hyyppä,et al.  Automatic detection of buildings from laser scanner data for map updating , 2003 .

[20]  Michael Brady,et al.  Vision-based Detection of Kerbs and Steps , 1997, BMVC.

[21]  Sergiu Nedevschi,et al.  Curb Detection Based on a Multi-Frame Persistence Map for Urban Driving Scenarios , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[22]  George Vosselman,et al.  DETECTION OF CURBSTONES IN AIRBORNE LASER SCANNING DATA , 2009 .

[23]  S. J. Oude Elberink,et al.  Feasibility of facade footprint extraction from mobile laser scanning data , 2011 .

[24]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

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

[26]  Nizar Abo Akel,et al.  Dense DTM Generalization Aided by Roads Extracted from LiDAR Data , 2005 .