at appeared in
do not remove
Is of the points
om one class to
inchanged. The
5D model was
f the bare earth
ations of 2.5D
(Fig. 4) was
1e was to detect
o low or where
terrestrial lidar
ese areas. The
to improve the
Figure 5. Gaps in aerial lidar data. (colors according strip
number).
2.2 Terrestrial lidar data
A local network was measured consisting of 13 points linked to
Fontalba GPS point (290079002). This point was measured
from Puig d'Estremera geodetic point (288080001) and
Llívia GPS permanent station (284074001).
Five sites were selected to station the terrestrial scanner in front
of the areas showing important gaps in airborne data. The
terrestrial lidar survey was done during two days, on September
8^ and 9", 2003. Target reflectors were installed and their
coordinates were measured with GPS and total station. The
known coordinates of the targets allowed for a first
approximation to the point cloud orientation of each scan but,
as they were closer than the area to measure, the angular
accuracy of this orientation was poor. In order to improve this
preliminary orientation, surface matching was employed. A grid
surface was computed for each terrestrial scan scene and
another was computed from the aerial points classified as
ground in the 2.5D model. This last surface was considered as
the reference surface. The orientation of each terrestrial scan
scene was adjusted to match the reference surface obtained
from the airborne lidar points. For each terrestrial lidar point
cloud a translation and a rotation were computed to minimise
the distance between the corresponding grid and reference
surfaces. This process was done with Polyworks software from
the company Innovmetric.
Once the orientation of the terrestrial points had been refined
they had to be classified but the available software was not able
to process data in almost vertical walls. The slope filter assumes
that the terrain slope is not too high and those points that
increase the surface slope over a certain threshold are supposed
to belong to the vegetation. This assumption failed completely
in this area. To circumvent this limitation a global rotation was
applied to all the lidar points to reduce the average slope of the
terrain. The point cloud was rotated by 30? around an axis
approximately parallel to the railway track. After that, it was
possible to add points to the previous set of ground points by a
fast editing procedure using the standard tools available in
TerraScan. The amount of available ground points in areas with
data gaps increased and the model improved (Fig. 4). After
editing, the inverse rotation was applied and all the points that
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
had been classified as ground were used to build a 3D
triangulated surface model.
i d AL Uo
2 es 2 A x nes ae
Figure 6. Gaps covered with terrestrial lidar data.
A dynamic survey was also carried out with the terrestrial laser
to acquire some additional information about the rail path. Data
was captured with the terrestrial LIDAR instrument integrated
in the GeoMóbil, a Land Based Mobile Mapping System
(Talaya et al. 2004a and Talaya et al. 2004b). The GeoMobil
was mounted in a train platform that was driven by the train. In
order to collect different parts of the track various paths were
completed with the scanner mounted in different orientations.
The GeoMobil system includes GPS/IMU sensors for the direct
orientation of the terrestrial laser scanner and of two digital
frame cameras. As the static laser campaign proved to be
enough to fill the data gaps, the dynamic laser survey was not
used in this project.
3. RESULTS AND CONCLUSSIONS.
Aerial and terrestrial lidar have been complementary in this
project. Aerial lidar data has a high precision in height on flat
areas and it is expected that its precision will decrease with
slope due to the worse precision of angular measurements and
footprint size. The usually achieved accuracy in elevation in flat
areas is around 10 cm. In contrast, the standard deviation of the
points in Easting and Northing was expected to be around 65
cm. This figure was computed from the relation o-H/2000 were
H is the height above ground according to system specifications
from Optech. The footprint has a diameter of approximately 26
cm from 1300 m above ground (Baltsavias, 1999). The largest
error source for the terrestrial lidar is the footprint size. At a
distance of 300 m, the beam divergence of 3 mrad corresponds
to a footprint diameter of 90 cm. Angular errors are less
important. The elevation angle is measured with a resolution of
0.036? and the azimuth with 0.018?. At this distance, the
precision in elevation is 9 cm while in azimuth it is twice that
value. Precision in range is 2.5 cm. Both lidar systems had a
better precision in the laser direction. The almost vertical
mountain walls were scanned from sites in front of them. It is
expected a high accuracy of terrestrial lidar because the laser
ray direction was close to the surface normal. Combining aerial
and terrestrial lidar it has been possible to obtain a product of
better quality than achievable using only one of these
techniques.