838 
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
5. MATCHING ENTITIES 
Until late eighties of the previous century, photogrammetric 
measurements were still based on point-wise measuring 
procedure. Point-wise approach requires that measured image 
points in one image have to be identified on another image(s). 
These point measurements must be conjugate and corresponds 
to exactly the same 3D object point. The mathematical formula 
for expressing image to object correspondence is based on 
collinearity equation. The other approach of introducing higher 
order primitives, usually referred as feature based assumes that 
the measured points belong to certain object feature, which 
fulfils a certain path in 3D space. The attractiveness of applying 
feature based approach is that point to point correspondence is 
no longer required. Control linear features can be used in all 
photogrammetric procedures, in single image resection and 
triangulation. The use of linear features is applicable in 
mapping of either: man-made areas where plenty of buildings, 
and road edges; or natural areas where river bank lines, coast 
lines and vegetation boarders can be involved as linear features 
(Heikkinen, 1992). Image space linear features are easier to 
extract than point features. Meanwhile, object space linear 
features can be directly derived form LMMS, GIS databases, 
and/or existing maps, (Habib and Morgan, 2003). 
Yet, the inclusion of linear features provide more constrained 
solution for the exterior orientation parameters, and better 
distortion modelling, however in some applications, is an option 
like for example camera calibration. Meanwhile, the inclusion 
of linear features becomes necessity, when they become the 
only possible (realistic) similarity entities for example in case 
of integrating LiDAR and photogrammetric data (Habib et al., 
2004b). Another example that demonstrates the importance of 
involving linear feature constraints in photogrammetric 
triangulation is the multi-sensor triangulation, where various 
sensory data with different geometric resolution are involved. 
In this case, the measurements of conjugate linear features 
constitute a great advantage, for their ease identification in 
different resolution images due to their high contrast (Habib et 
al., 2004a). The linear feature constraint, see Figure 3, is 
expressed mathematically as follows: 
\V x ®V 2l 
(2) 
Similar to point features, linear features can be classified into 
tie and control lines. Linear features, extracted from LiDAR 
data, are control lines and can be used in different 
photogrammetric operations. Linear features are useful to 
relative orientation only when the same line is observed in an 
image triplet (at least three images). In this case, the produced 
planes will intersect in the object line indicating the quality of 
fit. 
The framework, introduced in this paper, involves linear feature 
constrain since LMMS, with multi camera system, are usually 
operated over road networks, where plenty of straight road 
edges and lane line markings are available. The same features 
will be mostly visible in AMMS images. Additionally, some 
object space realistic constrains can be applied on linear 
features like horizontal/vertical lines, and parallelism of object 
lines. Downtown blocks have plenty of these constrains like 
building edges and lane line markings. These constraints help in 
the estimation of some of the georeferencing parameters. Thus, 
linear features present major matching entities in the proposed 
framework. 
Figure 3: Linear Feature Constrain 
The implementation of the linear feature constraint can be 
implemented in a number of ways. The two vectors VI and V2 
(in Figure 3) can be either replaced by their corresponding 
object space vector that'joins the perspective centre and the 
end/start point. Alternatively, they can be replaced by their 
corresponding image space vector after back projecting the line 
end points to the image space. Image space approach is much 
efficient than the object space as it provides much higher 
convergence rate than the object space cost function. This may 
be attributed to the consistency of the system of equations with 
those equations coming from collinearity constrain. 
6. FRAMEWORK 
The proposed framework for performing such integration 
scenario is considered as a generic bundle adjustment that can 
be easily extended to include additional matching entities and 
also to add any functionality if necessary. In order to make the 
developed framework fits the integration between mobile 
mapping data, many features have to be added. 
One of the basic modification, being a multi-camera enabled, 
which is a must when involving land based data. This can be 
done by involving another rotation matrix as a function of 
boresight angles. Of course, any order of rotation is possible. 
Regardless of the order, the three rotation angles are included as 
parameters in the adjustment. A disadvantage of this procedure 
is that the addition of these angles necessitates rather 
fundamental changes to the implementation of the adjustment, 
as the collinearity equations become functions of six angles 
instead of just three. This, in turn, makes the linearization of 
the collinearity equations considerably more complex. 
However, the necessity of changing the adjustment model 
presents a good opportunity to re-parameterise the 
R M (rotation matrix between camera and mapping frame) 
rotation matrix in terms of the roll, pitch, and azimuth angles. 
This enables the values observed INS angles as well as their 
covariance to be included in the adjustment. 
Additionally, as compared to other optical-to-optical multi 
resolution fusion, the proposed frame work involves linear 
feature measurements implementation for reasons mentioned in 
section 5. Another reason for using linear feature which is 
tightly related to the land based mobile mapping system 
bridging is that using linear feature has the advantage of