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ISPRS Commission III, Vol.34, Part 3A , Photogrammetric Computer Vision“, Graz, 2002 
  
Gyro sensors and/or other navigation systems. Range data 
obtained in different viewpoints are registered and integrated, 
and a completed model of urban environment is reconstructed. 
There are several drawbacks of stationary systems. First, in data 
acquisition, successive range views have to keep a degree of 
overlay, so that location and direction of viewpoints can be 
traced (or refined) by registering range data. Planning for 
viewpoints and directions in data acquisition becomes difficult 
when measuring large and complicated scene, since a balance 
between the degree of overlay and the number of viewpoints has 
to be decided according to both target objects and registration 
method. Secondly, there is still no registration method that 
could succeed in automatically registering range data of all 
kinds. When the number of range views increases, registration 
while keeping necessary accuracy becomes difficult. Updating 
stationary systems to moving platform ones (called vehicle- 
borne system) for reconstructing 3D model of large real scene is 
very important. 
Konno et al.[11] developed a sensor system by mounting three 
single-row laser range scanners on a vehicle with a high 
accurate navigation system. In this research, we propose a 
prototype of reconstructing the urban outdoor environment from 
the output of the vehicle-borne sensor system. 
2. OUTLINE OF THE RESEARCH 
In this chapter, we first briefly introduce the sensor system, and 
its data output, then state the problems, and finally outline the 
concept of the research. 
2.1 Sensor system 
In the sensor system developed by Konno et al.[11], three laser 
range scanners (LD-As) are mounted on a measure vehicle 
(GeoMaster), which has been equipped with a 
GPS/INS/Odometer based navigation unit (see Figure 1). LD-A, 
produced by IBEO Lasertechnik, is a single-row laser range 
scanner. It has a profiling rate of 10Hz, a maximal range 
distance of 100 meters, and a measurement error of 3cm. In 
each profiling (scan line), 1200 range distances are measured 
equally in 300 degrees, where 60 degrees of blind area exists 
due to hardware configuration. Reason for using three LD-As is 
to reduce occlusions by trees and other obstacles. As the vehicle 
moves ahead, LD-As keep profiling the surroundings on three 
different vertical planes (cross-section). Meanwhile, the 
navigation unit outputs the vehicle's location coordinates (x, y, 
Z) and orientation angles ( , , ) in world coordinate system 
at the moment of each laser scanning, so that all range distances 
(range points) in LD-A’s local coordinate system at the moment 
of measurement can be geo-referenced to the world coordinate 
system. Range points of different LD-As are recorded in 
different output files (views) in the order of measurement 
sequence. 
2.2 Problem statement 
This research focus on generating a surface representation of 
urban out-door environment using the range outputs of the 
above sensor system. Surface reconstruction from dense range 
data has been studied for decades. Soucy and Laurendeau, [16] 
and Turk and Levoy, [18] exploited the connectivity of 
structured range points. Hoppe, ef al [8] proposed a method of 
generating an implicit surface from unorganized points using 
volumetric representation and marching cube algorithm. Curless 
and Levoy [2], Wheeler et al.[19] are the hybrids of the above 
two methods, where implicit surface method is exploited to 
integrate structured range views. Most of the researches assume 
that all the range points are on or near an implicit surface, and 
they are clear or only have a systematic error. However this is 
not always true in urban out-door environment. 
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Moving ahead 
   
(a) Sensors’ alignment (b) Pictures of the vehicle 
Figure 1. Sensor System 
Except the irregular points that reflected by passing cars and 
pedestrians, window glasses and trees are two major difficulties 
in the modelling of urban out-door environment by laser 
scanning. Some window glasses are penetrative to laser beam, 
subsequently yields range measurement of unknown indoor 
objects, which are beyond our interest. While some window 
glasses give mirror reflection, so that yields black holes (no 
data) on building surface. Trees are of complicated shape and 
plenty of occlusions. Laser scanning of a tree yields a cloud of 
scatter points, which are not implying a surface but a volume. It 
is obvious that modelling a surface-structured object, such as 
building and road surfaces, should be conducted in a different 
level with that of a volume-structured object, such as trees. 
However trees are always near to and block the measurement of 
building, borderline between them is always confusing. 
2.3 Outline of the research 
Reconstructing a surface representation of urban out-door 
objects is conducted in two procedures. First, range points are 
classified into six groups, ie. the measurement of vertical 
(building) surface, false window area, road surface, other kinds 
of surface, tree and unknown objects. False window area 
(briefly referred to “window area” in the following sections) 
implies the penetrative or mirror-like window area, of which 
range values are corrected and interpolated using the 
measurement on surrounding vertical building surfaces. In this 
research, window area that does not yield erroneous or false 
reflections is not our research interest. They are regarded as a 
part of vertical building surface. Secondly, volumetric 
representation and marching cube algorithm are exploited since 
it is easy in generating a model of desired level of detail, which 
is required in many 3D GIS applications. The scheme for 
generating volumetric representation and the algorithm for 
computing signed distance are defined differently, where iso- 
surfaces are computed for surface-structured objects, i.e. 
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