COMMON ADJUSTMENT OF LAND-BASED AND AIRBORNE MOBILE MAPPING 
SYSTEM DATA 
Taher Hassan, and Naser El-Sheimy 
Mobile Multi-sensor Research Group 
Department of Geomatics Engineering, 
The University of Calgary 
2500 University Dr. N.W. Calgary, Alberta, Canada T2N 1N4 
Tel: (403) 220 7587, Fax: (403) 284 1980 
E-mail: tfabbas@ucalgary.ca, and elsheimy@ucalgary.ca 
KEY WORDS: Photogrammetry, Mapping, Bundle Adjustment, Fusion, Georeferencing, Mobile Mapping, and Triangulation. 
ABSTRACT: 
Presently, numerous types of geospatial data have become available for both industrial and research use. These types of data are 
collected by different sensors and from different platforms. Therefore, they are substantially different in physical nature, 
amount/type of information, and geometric/radiometric resolutions. Multi-sensor data fusion techniques combine data from multiple 
sensors and related information from associated databases, to achieve improved accuracies and more specific inferences than could 
be achieved from a single sensor. Many researchers have proposed different fusion schemes for integrating different sensory data. 
Yet, there is less amount of attention towards the fusion of airborne (AMMS) and land-based (LMMS) mobile mapping systems 
imagery/navigation data. Although, this integration scheme may be thought to be simple or similar to other optical-to-optical 
registration process, its practical implementation carries many challenges. Images captured by (LMMS) and (AMMS) are different 
in the sense of direction, scale, coverage, hidden/visible features. Consequently, the integration between the data captured by 
AMMS and LMMS is of high potential since both image/navigation data sets are complementary and can be integrated to complete 
the picture about the earth’s surface. This paper proposes a fusion scheme for the overall objective of improving the 3D mapping 
accuracy. This fusion scheme aims at creating a unique model, which can be visualized in several contexts of application.Also, the 
common adjustment of terrestrial and aerial photogrammetric networks recovers/enhances sensors georeferencing information-a 
way for LMMS bridging during GPS outages or georeferencing aerial images block. The proposed integration framework uses 
different matching entities of lines (e.g. road edges and lane lines) in addition to the traditionally-used point-based approach. 
Mathematical model of collinearity condition has been adapted to suite multi-camera system. In this paper, we consider the scientific 
and the technical issues about the strategy of the proposed fusion scheme. The used modalities will be coming from the simulated 
VISAT LMMS platform and other sources. 
1. INTRODUCTION 
The number of sensors, orbiting our globe, is growing steadily 
over the years. Data, collected by such sensors and from 
different platforms, are considerably different in physical nature, 
amount/type of information, and geometric/radiometric 
resolutions. It is firmly confirmed through plethora of 
researches that no single sensory data can provide the optimal 
solution for a specific query. Consequently, data 
fusion/integration is considered for a better solution/information 
recovery about the object being captured. The topic of multi 
sensor data fusion has received a lot of attention over the years 
by the scientific community for military and non-military 
applications. Many researchers have proposed different fusion 
schemes for integrating different sensory data. However, there 
is less amount of attention towards the fusion of 
imagery/navigation data of airborne and land-based mobile 
mapping systems. 
Mobile Mapping Systems (MMS) can be classified according to 
the physical carrier into land (LMMS), and airborne (AMMS) 
based systems. Images captured by (LMMS) and (AMMS) are 
different in the sense of direction, scale, coverage, 
hidden/visible features. Consequently, the integration between 
the data captured by the two systems is of high potential hence 
both image/navigation data sets are complementary and can be 
integrated to complete the picture about the earth’s surface. 
In this paper, we introduce a novel scheme for the fusion 
AMMS and LMMS data through adapting many of the existing 
tools to fit the special requirements of such fusion scenario. The 
investigation and the drawn conclusions are based on 
simulating modalities from the VISAT platform and other 
sources. This research aims at creating a unique model, e.g. 
facets-based, which can be visualized in several contexts of 
application and serves different scientific communities. In 
section 2, the general benefits of the data fusion are listed, and 
some exampled are illustrated. Section 3 is devoted to discuss 
the potential for the proposed fusion scheme. Both sections 4 
and 5 describe the math models for matching entities after 
applying the necessary adaptations to fit the proposed fusion 
scheme. Section 6, draw the main special requirements and 
features of the fusion scheme framework. The preformed 
simulations are described in section 7 as well as the obtained 
results. Conclusions are drawn in section 8. 
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