The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
194 
shows the results for the ramp area pictured in Figure 3. The 
transformation parameters between these two point sets (the 
original LiDAR-derived points and their corresponding points 
on the control curve) are calculated in a least squares 
adjustment. Table 1 shows the 2D transformation parameters 
for three different cases, clearly indicating the robustness of the 
ICP method with respect to noisy data, such as using the 
original LiDAR points. The differences between curves and 
residuals after ICP matching for the three cases are shown in 
Table 2. The 2 cm horizontal precision is realistic, given the 
fact that the GPS-surveyed points are known at a 1-2 cm-level 
accuracy, and the LiDAR-based pavement marking positioning 
accuracy is estimated at the few cm range. The 9-10 cm 
precision terms in case 2 correspond to the use of the noisy 
LiDAR data (no curve-fitting applied to smoothly model the 
pavement markings). 
Table 2. Original differences and residuals after ICP (2D). 
7. CONCLUSION 
The new method that introduced the use of pavement markings 
as LiDAR ground control delivered encouraging initial results. 
The performance of the three main processing steps, 
including the extraction of pavement markings, curve fitting, 
and ICP-based matching has been validated. Using a dataset 
acquired over a transportation network by a state-of-the-art 
LiDAR system, pavement markings from several intersections 
and freeway ramps have been processed delivering robust 
results. In particular, the performance of the ICP matching 
algorithm is noteworthy. 
ACKNOWLEDGEMENT 
Figure 10. ICP matched curves; magenta: curves fitted to 
control points, red: GPS control points, cyan: LiDAR point and 
curves fitted, and blue: matched points. 
ICP input data 
ICP-adj usted 
transformation parameters 
AX [m] 
AY [ml 
<Pl°l 
Both, LiDAR and GPS points are 
curve-fitted 
0.153 
-0.114 
0.000 
No fitting of LiDAR points, GPS 
points curve-fitted 
0.150 
-0.114 
0.000 
No fitting of GPS points, LiDAR 
points curve-fitted 
0.158 
-0.116 
0.000 
Table 1. Transformation results (2D). 
Case 
Differences/Residuals 
X 
m] 
Y [ml 
Before 
After 
Before 
After 
mean 
Std 
mean 
std 
mean 
std 
mean 
Std 
1 
0.16 
0.02 
0.00 
0.02 
-0.11 
0.02 
0.00 
0.02 
2 
0.16 
0.10 
0.00 
0.10 
-0.12 
0.09 
0.00 
0.09 
3 
0.16 
0.02 
0.00 
0.02 
-0.12 
0.01 
0.00 
0.01 
The authors would like to thank the Ohio Department of 
Transportation for the GPS survey of pavement markings and 
flying LiDAR surveys to acquire essential data for this research. 
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