2-5-1 
TOWARDS AUTOMATED PROCESSING OF MOBILE MAPPING IMAGE SEQUENCES 
C. Tao, M. A. Chapman, and N. El-Sheimy 
Dept, of Geomatics Engineering, The University of Calgary 
2500 University Dr., NW, Calgary, Alberta, Canada, T2N 1N4 
Tel: (403) 220-5826, Fax: (403) 284-1980 
E-mail: ctao@ucalgary.ca, chapman@fuzzy.ensu.ucalgary.ca, elsheimy@ucalgary.ca 
B. Chaplin 
LH Systems LLC 
10965 Via Frontera, San Diego, California 92127 USA 
E-mail: Bnice.Chaplin@lhsystemsgroup.com 
KEY WORDS: Mobile mapping, visual motion analysis, multiple image matching, image sequences analysis, terrestrial 
triangulation, feature extraction, and deformable models. 
ABSTRACT 
The development of automated approaches to image sequences processing using mobile mapping images has been one of the 
research focuses in the Department of Geomatics Engineering at The University of Calgary. This paper presents an overview of the 
state-of-the-art technologies in this area, and the methods developed for the VISAT mobile mapping system at The University of 
Calgary. 
Following a brief overview of the mobile mapping system technology, an analysis of mobile mapping image sequences using 
computerized visual motion theory is given. It has been recognized that the use of valid scene constraints is of particular importance 
to the success of the development of automated methods of image sequences processing. The constraints from both image domain 
and object domain have been developed. Based on the use of these constraints, Several key methods to automated processing of 
mobile mapping image sequences are proposed and implemented. These methods developed can be grouped into two categories, 
namely, information extraction and image bridging. Three typical algorithms for automated or semi-automated information 
extraction are described. They are semi-automated object measurement using multiple image matching, automated reconstruction of 
road centerlines from long image sequences, and map guided automatic verification of transportation objects with vertical line 
features. Image bridging deals with the estimation of the orientation parameters of the imaging system from the sequence of time- 
varying stereo imagery in the absence of both GPS and INS data. Both the automated feature and image matching techniques are 
utilized to tie the image sequences for triangulation. 
1. INTRODUCTION 
The core technologies of remote sensing, photogrammetry, 
geographic information systems (GIS) and spatial positioning 
are becoming fully integrated, resulting in the tremendous 
expansion and rapid growth of mapping markets. Modern 
digital mapping technology is characterized by the capabilities 
of multi-discipline combinations, multi-platform 
compensation, multi-sensor integration and multi-data 
fusion. 
Multi-platform, and multi-sensor integrated technology have 
established a trend towards spatial data acquisition. Multi 
sensor systems can be mounted on various platforms, such as 
satellites, aircrafts or helicopters, land-based vehicles, water- 
based- vessels, and even hand-carried by individual surveyors. 
As a result, every vehicle or individual surveyor becomes a 
potential data collector, responsible for globally integrated data 
acquisition. The recent development of land-based mobile 
mapping systems represents a typical application of this 
integrated technology. 
In general, there are three fundamental issues involved in 
mobile mapping technology: • 
• Accurate and reliable georeferencing of image sequences 
captured from a mobile mapping vehicle, 
• Rapid and accurate information extraction from 
georeferenced image sequences, and 
• Complete representation and efficient management of the 
spatial and attribute information collected. 
The development of an advanced navigation capability is a key 
to unlocking the full potential of mobile mapping systems. 
Accurate and reliable georeferencing relies on integrated 
navigation techniques. The integration of GPS (Global 
Positioning Systems) with other navigation sensors, such as INS 
(Inertial navigation systems) and DR (dead reckoning), has 
defined a trend for mobile mapping georeferencing. The 
research has been extensively conducted by El-Sheimy (1996), 
Schwarz et al. (1993a), and Wei and Schwarz (1990). The 
recent advancement of the use of expert knowledge to optimize 
the data flow and automate the data acquisition of the VISAT 
mobile mapping system is addressed by El-Sheimy et al (1999). 
An efficient database engine must be designed to handle the 
sequential stream of information. Object-oriented data 
representation and network-based data dissemination are very 
suitable for these types of input. The research work on building 
an object-oriented 3-D GIS using mobile mapping data can be 
found in Qian (1996). The implementation of a SQL engine for 
building 3D databases that can be easily used directly or 
imported to any GIS software is described in Chapman et al. 
(1999).