The International Archives of the Phutogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
field survey prior to data acquisition and their establishment is 
typically expensive and time-consuming. Also, for many 
terrestrial surveys, the establishment of sufficient control points 
is virtually impossible. For example, consider the control 
requirements to map an entire city using close-range 
photogrammetry. Finally, for some mapping sensors such as 
laser scanners or push-broom CCD arrays (Line CCD camera), 
it is difficult or impossible to establish control. The use of these 
sensors is not practical unless direct-georeferencing is 
performed. 
1.2 Related Works 
The first operational land-based MMS was developed by the 
Centre for Mapping at the Ohio State University. Their system 
(called GPSVan) integrated a code-only GPS receiver, two 
digital CCD cameras, two colour video cameras and several 
dead reckoning sensors (Goad, 1991; Novak, 1991). All 
components were mounted on a van. The GPS provided the 
position of the van and the images from the CCD cameras were 
used to determine the positions of points relative to the van. The 
dead reckoning sensors, which consisted of two gyroscopes and 
an odometer (wheel counter) on each of the front wheels, were 
primarily used to bridge GPS signal outages. These sensors 
were also used to provide orientation information for the 
exposure stations; however, there is little - if any - published 
information that examines the orientation accuracy of the 
GPSVan’s dead-reckoning sensors and the poor accuracy of 
similar sensors suggests that the orientation information they 
provided would have been of marginal quality at best. The two 
video cameras were used solely for archival purposes and to aid 
attribute identification - no relative positioning was performed 
from the video imagery. Using bundle-adjustment techniques 
with relative-orientation constraints, the GPSVan was able to 
achieve relative object space accuracies of approximately 10cm. 
Unfortunately, because only carrier-smoothed code-differential 
GPS was used, absolute object-space accuracies were limited to 
1-3 m. 
GPSVan successfully illustrated how land-based multi-sensor 
systems could improve the efficiency of GIS and mapping data 
collection. However, ©Due to poor navigation sensors, the 
absolute accuracy of the object space points was too poor for 
many applications, especially when compared with competing 
technologies. ©Because GPSVan were simple stereovision 
system as its mapping sensor, automatic extraction of objects is 
difficult or impossible. Therefore, further developments of land- 
based mobile mapping system focused on these two points ®to 
increase absolute object space accuracies by using more high 
accuracy hardware or using more sophisticated processing 
techniques; ©to equip more mapping sensors for enabling more 
flexible data collection , better imaging configuration of objects 
and easier object extraction and recognition automatically. 
Based on the two points, the following expression is extended 
by introducing contributions of various research groups. 
Works to improve/stabilize mapping accuracy 
The obvious techniques for improving absolute object space 
accuracy were to use carrier-phase GPS, while the obvious 
choice accuracy dead-reckoning sensor was a high precision 
IMU. The use of an IMU has an additional advantage over other 
types of dead-reckoning sensors in that it also provides high- 
accuracy orientation information for the exposure stations. 
Later land-based MMS added dual-frequency carrier-phase 
differential GPS, more accurate IMUs, and more sophisticated 
processing techniques for navigation sensor data. Typical 
example system is the VISAT™ system (Schwarz et al., 1993). 
VISAT had absolute object space accuracies that had previously 
been unattainable (about 30 cm with absolute accuracy). 
This 30Cm accuracy looks very excellent, but this accuracy is 
not direct from navigation sensor, and it should be do “image- 
based bundle adjustment” to improve georeferencing accuracy 
of image like triangulation of aerial photogrammetry (Schwarz 
et al., 1993). So it can be said that bundle adjustment is efficient 
method to improve accuracy, but it is difficult to do it full 
automatically duo to fallible image matching for tie point. 
The other intelligent techniques for improving absolute object 
space accuracy were to use more sophic data processing of 
multiple navigation sensors like GPS, IMU, and ODO. This 
works can be found in Geomobil (Talaya 2004) and 
StreetMapper (Hunter 2006). The Geomobil system use 
POS/LV navigation sensor and it’s corresponding software- 
Pospac, so that the absolute accuracy can reach to 30 cm.. The 
StreetMapper use TERRAcontrol navigation sensor system by 
IGI, and the accuracy also can get the same level of Geomobil. 
But, however, this level accuracy is should keep system in good 
GPS signal condition. 
Works to extract object as automatically as possible 
For more flexible data collection, better imaging configuration 
of objects and easier object extraction and recognition 
automatically, the obvious methods is to equip more kind type 
of sensor such as laser sensor, which can get 3D geometry 
information of object surface (see Reed et al 1996, and Talaya 
2004). But because there are also disadvantages of laser sensor, 
the further development focus on fusion of CCD camera, which 
can get color information, and laser. This kind of research can 
be found in Zhao 2003. GeoMaster™ in research of Zhao 2003 
was notable because of fusion of laser range finder data and 
CCD line camera. Where previous land-based MMS were 
simple stereovision systems employing only two forward facing 
cameras, GeoMaster™ have 6 CCD line camera and 3 laser 
scanners that enable more flexible data collection and better 
imaging geometry. But Due to easy geometry deformation of 
line camera, automatic recognition of object becomes difficult. 
The mobile systems which are equipped with laser sensor and 
vision sensor also can be found in following research groups, 
such as Geomobil (Talaya 2004), StreetMapper (Hunter 2006) 
and Waseda system (Ishikawa 2006). But, however, these 
systems are just to use two kinds of data for different object, not 
to fuse them for detect and extract same object for automatic 
processing by overcome the drawbacks of individual sensor. 
But it is obvious that fusion of multiple mapping sensors is 
efficient method to detect and extract object because data fusion 
processing can overcome the drawbacks of camera or laser 
sensor. 
1.3 Our Research Objective 
Based on the above discussion, the primary objective of this 
paper is to develop a serial of methods for collecting more 
accuracy, higher density (called as high-definition) spatial 
information data of road objects with more rapid and less costly 
by fusing vehicle-based navigation data, vision data and laser 
range data. 
The system based on our developed methods can overcome the 
drawbacks of current mobile mapping systems - namely not 
high position accuracy and not high ratio of automatic mapping