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USING ROAD PAVEMENT MARKINGS AS GROUND CONTROL FOR LIDAR DATA 
C. Toth 3, *, E. Paska 3 , D. Brzezinska b 
3 Center for Mapping, OSU, 1216 Kinnear Road, Columbus, OH 43212 USA - (toth, eva-paska)@cfm.ohio-state.edu 
b Dept, of Civil and Environmental Engineering and Geodetic Science, OSU, dbrzezinska@osu.edu 
Commission I, WG 1/2 
KEY WORDS: LiDAR, LiDAR intensity, Feature extraction, QA/QC 
ABSTRACT: 
LiDAR technology, the primary source of highly accurate surface data at large scale, has seen remarkable developments in recent 
years. Specifically, the accuracy of the laser ranging has reached the few cm level for hard surfaces, close to static survey 
performance, and the point density has increased significantly, as a result of higher pulse rates, such as 150 kHz PRF for multipulse 
LiDAR systems. The high ranging accuracy of the laser sensor also means that the overall accuracy of the point cloud is now 
predominantly determined by the quality of the navigation solution (typically based on GPS/IMU sensor integration), which is also 
advancing. All these developments allow for better surface representation in terms of denser point cloud with highly accurate point 
coordinates. Furthermore, because of the increased point density, the horizontal accuracy has become an equally important part of 
the product characterization. In parallel to these developments, the demand for better QA/QC is also growing, and now the 
characterization of the LiDAR products includes the horizontal accuracy. Except for relative measures, there is no reliable way to 
assess the positioning quality of the data captured by any imaging sensor system, which is based on direct georeferencing, and 
therefore, using some ground control is almost mandatory if high accuracy is required. This paper introduces a method to use road 
pavement marking as ground control that could be used for QA/QC. These linear features are widely available in urban areas and 
along transportation corridors, where most of the government and commercial mapping takes place, and an additional advantage of 
using pavement markings is that they can be quickly surveyed with various GPS echnique (RTK, VRS, post-processed). 
1. INTRODUCTION 
The evolution of ground control used for product QA/QC is 
closely related to the improvements in the LiDAR point density. 
When sparsely distributed points were available, the vertical 
accuracy was the only concern (ASPRS Guidelines, 2004). In 
fact, the horizontal characterization was greatly ignored at the 
introduction of LiDAR technology. Obviously, from a 
theoretical point of view, points separated by a few meters did 
not allow for adequate surface characterization in general, 
except for flat areas. To assess the vertical accuracy of the point 
cloud, flat horizontal surfaces with precisely known elevations 
can be used. Once the vertical difference was measured, usually 
based on the statistics derived from a sufficient number of 
points over flat surface patches, either a simple vertical shift 
was applied as a correction, or a more complex model could be 
used that factored in surface differences observed at several 
(well distributed) locations. 
As the LiDAR market started to grow rapidly, soon the LiDAR 
systems showed truly phenomenal performance improvements. 
In less than five years, the pulse rate improved by an order, and 
now 100 and 150 kHz systems are widely used (Optech, 2006 
and Leica, 2006); in addition multi-pulse systems are also 
available. More importantly, the ranging accuracy has increased 
substantially and now stands close to the level of static GPS or 
short baseline kinematic surveys, i.e., 1-2 cm for hard surfaces, 
which is practically negligible to the typical navigation error 
budget. This remarkable performance potential of the newer 
LiDAR systems, combined with better operational techniques, 
opened the door toward applications where large-scale or 
engineering-scale accuracy is required. At this point, the 
georeferencing error budget and, to a lesser extent, the sensor 
calibration quality, are critical to achieving engineering design 
level accuracy (few cm). Using ground control is an efficient 
way for independent and highly reliable QA/QC processes and, 
if needed, to compensate for georeferencing and sensor 
modeling errors. 
The errors in laser scanning data can come from individual 
sensor calibration or measurement errors, lack of 
synchronization, or misalignment between the different sensors. 
Baltsavias (1999) presents an overview of the basic relations 
and error formulae concerning airborne laser scanning. Schenk 
(2001) provides a summary of the major error sources for 
airborne laser scanners and error formulas focusing on the 
effect of systematic errors on point positioning. More recently, 
Csanyi May (2007) presents a comprehensive analysis on 
LiDAR error modeling. In general, LiDAR sensor calibration 
includes scan angle and range calibration, and intensity-based 
range correction. The LiDAR sensor platform orientation is 
always provided by a GPS/IMU-based integrated navigation 
system. The connection between the navigation and LiDAR 
sensor frames is described by the mounting bias, which is 
composed of the offset between the origin of the two coordinate 
systems and the boresight misalignment (the boresight 
misalignment describes the rotation between the two coordinate 
systems, and is usually expressed by roll, pitch and heading 
angles). To achieve optimal error compensation that assures the 
highest accuracy of the final product, all of these parameters 
should be calibrated. Since not all of the parameters can be 
calibrated in a laboratory environment, a combination of 
* Corresponding author.