CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: EXTRACTING BUILDING FOOTPRINTS FROM 3D POINT CLOUDS USING TERRESTRIAL LASER SCANNING AT STREET LEVEL Karim Hammoudi, Fadi Dornaika and Nicolas Paparoditis

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 highest vote for each cluster. We propose to employ a more accu rate method such as the RANSAC method to model the building footprint. One advantage of our approach is the following. A curved facade will be approximated by a single line. In addition to this, much information deduced by the clustering step allow to automatically adjust the parameters of the RANSAC algorithm and to thus improve the precision of the detected lines. The RANSAC method is commonly used to detect lines among edge points (Bretar and Roux, 2005) and (Sester and Neidhart, 2008). We use a classic method (Fischler and Bolles, 1981). For each detected cluster, we use the following process. We use the original space of parameter (x,y). Two different points belong ing to the cluster are randomly selected, characterizing a line. A neighborhood is defined along this line by a minimal distance be tween a point and the line. This process is iteratively repeated until the number of inlier points in the neighborhood is maxi mized. In our approach, the number of facade points is roughly equal to the number of points for each cluster. Furthermore, the minimal distance associated with the RANSAC technique can be determined from the dispersion of the cluster. When this step is carried out, the best fit lines of 2D points are extracted us ing the Least Squares Adjustment (LSA technique). A set of 2D segments giving the building footprint is then obtained from the detected 2D lines. 4 PRELIMINARY RESULTS The acquired 3D data correspond to the facades of buildings in the 12th district of the city of Paris. In this study, we use a high precision 2D laser sensor LMS-Q120i made by RIEGL company 1 . The laser sensor is positioned on the roof of the vehicle. Its beam plane is perpendicular to the vehicle trajectory. The system allows us to carry out 10000 measurements per second and the beam vertically sweeps with an opening of 80 degrees (-20 to 60 degrees with respect to the horizontal). The angular precision of the beam is equal to 0.01 degrees. More specifically, the precision of laser-based measurements is approximately 3 cm at 150 m. In this study, the angular resolution was configured to 201 points by frame (see figure 7). The ground based laser range transmits laser pulses with simple echo. Figure 7: Acquisition of the 3D point cloud using the 2D laser sensor. The frame shows a selected band without occlusions. The raw measurements provided by the laser sensor are points that are parameterized by distance and angle. The reflected in tensity of the laser is between 0 and 1. The coordinates of the 3D points are expressed in the laser sensor coordinate system and also in a common coordinate system, namely the ground refer ence (absolute) Northern, Eastern and Altitude in Lambert 93. The precision of a 3D point is not easy to evaluate because it depends on the laser precision and on the referencing system pre cision. 1 1 Link to RIEGL company: http://www.riegl.com Figure 8: Two difficult facades for the classical Hough Trans form: i) a curved facade, and ii) a facade with a low density of 3D points. The detected lines correspond to the local maxima of the Hough space accumulation using the filtered cloud. The experiments are carried out on two building facades having different architecture and different density of acquired 3D points. One can thus assess the robustness and the efficiency of the pro posed approach. Figure 9: Extracting the building footprint lines using the pro posed approach. Figures 8 and 9 show the extraction of the building footprint lines using the classical Hough Transform and our proposed approach, respectively. The 3D point cloud is presented in the upper part of the figure. The projection onto the 2D accumulator is presented in the lower part of the figure. The studied building illustrates two difficult cases for a classical Hough Transform. Indeed, the left facade does not suffer from occlusions but it is slightly curved. On the other hand, the right facade which is a planar structure is partially occluded, that is, the density of its 3D points in the 2D accumulator space is much lower than that of the left facade. Cluster characterizing one building facade Extraction based on the score maximum in the cluster Extraction based on a reestimation by the RANSAC method Figure 10: Comparative schema illustrating the precision of lines detection step on one simulated facade.

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