871 
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
cloud can be generated, and stereo image sequences also can be 
registered for mapping. 
Laser Point Rendering and Image Positioning 
For better fusion processing, we should know the corresponding 
relationship between any point of laser 3D point cloud and 
image sequences, which is called as image linking. Based on 
the linking information, it is easy to know which images are 
including the appointed point of laser point cloud; and the 
position of any place of image by back-projected laser 3D point, 
which is called as image positioning. When the linked image 
has been calculated, the color information of that point also can 
be acquired from that image, which is called as laser point 
rendering. The point rendering technology gives color 
information to laser 3D point cloud so that laser point cloud 
based extraction can be performed well because of additional 
color information. 
For laser point rendering, it is possible that a laser point is 
covered by foreground object in that image so that error color 
information is acquired. Our solution is using stereo image 
checking. As you know, if a point is covered really by 
foreground object, the two projected points in image pair 
become non-matchable. Based on this checking, the color 
information can be searched in round stereo image pair until the 
two projected point can be matched (Detail in Shi 2008). 
using shape of laser data. The boundary point can be easily 
done if the road have curb. Because of the road curb, if height 
of a laser point suddenly changes, the boundary point is 
detected. But for these no-curb roads, the next processing- 
image and laser fusion —is used for boundary point detection. 
The detailed description for automatic extraction of road 
boundary and road mark is reported in the reference paper (Shi 
2008). 
Image and Laser Data Fusion based Final Robust 
Extraction 
Like road sign, the fusion method can detect it more robustly. 
Road signs use particular colors and geometric shapes to attract 
driver’s attention, so typical algorithms of image based 
detection and recognition use their inherent color, shape and 
texture to detect and to extract sign type and legend. On the 
other hand, road sign also stand alone in roadside, so it has 
inherent features in laser 3D space. Based on these features in 
laser 3D space, road sign can be easily detected. This step fuse 
laser data and image to detect as successful as possible. 
3.3 Success Extraction Ratio from Our Experiments 
In 10km experiment data, success extraction of road boundaries 
reaches to 95%; but for traffic mark, because of no-capture 
image data on curving place of road, the missed ratio reaches to 
about 13.4%. For reducing the miss ratio of traffic mark, system 
should capture images on curving place as dense as possible. 
For seeing effectiveness of fusion method for road sign 
recognition, we compare the fusion method with image method. 
(1) Fusion method can detect most of road sign (more than 94%) 
especially in sun day, in which image method just have about 
51% success ratio because of the effect of sun-shining. 
(2) In cloud condition, the recognition ratios of image method 
and fusion method are more than 90% too, but in sun-shining 
condition, fusion method has more recognition ratio than image 
method. Although the ratio is 71.4%, it is easy to check its type 
by extracted image (its extracted ratio of fusion method reach to 
94.3%). 
Whether 
Type 
Existing 
RS 
Detected 
Recognized 
Cloud 
Image 
161 
153 
(95.03%) 
145 
(90.06%) 
Image& 
Laser 
161 
157 
(97.5%) 
148 
(91.93%) 
Sun- 
shining 
Image 
35 
18 
(51.4%) 
16 
(45.7%) 
Image& 
Laser 
35 
33 
(94.3%) 
25 
(71.4%) 
Table 1. Success ratio of road sign extraction 
Figure4. Flowchart of Fusion Processing 
for Automatic Road Object Extraction 
Laser Point Cloud based Candidate Extraction 
Rendered Laser 3D Point Cloud not just has color information, 
but also it is seamless 3D data. Just because it is seamless, the 
extraction of linear object, such as road traffic lane mark, 
becomes less costly with “No-mosaic”. 
Typically, road surface is flat but pavement surface is higher 
than road surface. The feature just makes extraction possible by 
4. CONCLUSION 
In this paper, we aim to satisfy with the high demands for the 
road spatial data. We presents high efficient road mapping 
technology by fusing vehicle-based navigation data, stereo 
image and laser scanning data for collecting, detecting, 
recognizing and positioning road objects, such as road 
boundaries, traffic marks, road signs, traffic signal, road guide 
fences, electric power poles and many other applications 
important to people’s safety and welfare.