The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
870 
improved position of camera is then used as control point for 
refine navigation data and then the improved position and 
orientation for laser is calculated. 
2.3 Positioning Accuracy from Our Experiments 
Positioning accuracy is the integrated accuracy with navigation 
sensors and mapping sensors. Mapping accuracy of some GCPs 
is listed in comparison with terrestrial surveying, which is cm- 
level accuracy. It is believed that our system mapping accuracy 
can reach to about 30 cm order by our high-accuracy 
positioning technology by fusion processing of GPS/IMU, 
Stereo image sequence and laser point cloud. Our high- 
accuracy positioning method also acquires good position 
accuracy in the area of poor GPS signal and multi-path. 
3. AUTOMATIC ROAD MAPPING BY FUSING IMAGE 
AND LASER DATA 
-# 
The term automatic road mapping in this research is defined as 
to automatic extract, recognize and position road objects from 
collected data by vehicle-based mobile mapping system. The 
main topic in this section is to discuss how to automatically 
collect road spatial information by fusing stereo image and laser 
data which are captured by our vehicle-borne multiple sensor 
mobile mapping system. The keywords of this section are data 
fusion and automatic mapping; data fusion is processed for 
better automatic mapping, and automatic mapping is done for 
more efficient and less costly generating high-definition road 
spatial data. 
3.1 Why need fusion of image and laser data 
For automatic extraction and recognition, typically, there are 
image-based approach, laser-based approach, and fusion 
approach. Image-based approach base on digital image 
processing thesis, utilize color, shape, texture to recognize 
object. Image-based approach has very long development 
history; many of the techniques were developed in the 1960s at 
the Jet Propulsion Laboratory, MIT, Bell Labs, University of 
Maryland, and a few other places. Although well developed 
image-based approach has many successful applications, it has 
some inherent drawbacks as below: 
1. Occlusion problem own to it’s center perspective projection; 
2. Be sensitive to environment conditions 
such as light and shade; 
3. Mosaic from piece of images is costly (FIgure3-a); 
4. Stereo matching based 3D positioning is difficult 
for full automation. 
Laser scanner is active sensor, so that laser-based approach is 
insensitive to environment conditions. Because laser scan object 
through point by point, exclusion problem of laser-based 
approach is not so serious with comparison of image-based 
approach. And laser scanner can acquire the continuous 3D 
point cloud, so there are no mosaic problem and also no stereo 
matching problem. From the above analysis, it is obvious to say 
that laser-based approach just can solve those unsolvable 
problems by image-based approach. However, laser-based 
approach also has its inherent drawbacks such as no gray or 
color information so that recognition becomes difficult. BUT 
that is just strong point of image-based approach. 
The Figure 3 shows some typical merits of road object 
automatic extraction by fusion method, (b) demonstrates that 
color-rendered laser point cloud is easy to extraction road data 
without mosaic processing; (c) shows that color-rendered laser 
point data can be used for detecting road mark; (d)(e) show 
laser point cloud can support road object detection and 
recognition using its robust shape due to active sensor. 
Figure3. Comparison of image and laser data for automatic 
object recognition and extraction 
3.2 Automatic Road Object Extraction by Fusing Stereo 
Image Sequence and Laser Point Cloud 
From the above discussion, it is easy to know data fusion 
method can get over those inherent drawbacks of individual 
approach. Our developed approach is based on fusion of image 
and laser approach. The data origin is laser range data, stereo 
image sequences, and automatic acquiring of road spatial data is 
done by fusion method. The processing flowchart of fusion 
method is drawn in figure 4. The processing includes five key 
steps as 1.Direct geo-referencing; 2.Laser point rendering; 
3.Image Positioning; 4. Laser point cloud based candidate 
extraction using shape, color and position and 5. Image and 
laser data fusion based final robust extraction. 
Direct Geo-referencing 
Direct geo-referencing is to identify the spatial position of the 
objects scanned by mapping sensors at any time while the 
vehicle is moving with reference to a common coordinate 
system. The detailed information can be reviewed in chapter 5. 
By high-accuracy positioning technology, we have calculated 
high-accuracy instant sensor posture (X,Y,Z, (p,co,ic) when laser 
point or image is recorded. Based on the posture, laser 3D point