The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
LiDAR system calibration is done through several procedures. 
In the first step, individual sensors are calibrated in the 
laboratory. After that, mounting parameters are determined 
after installing sensors on a platform. In the last step, in-situ 
calibrations are done before and after surveying missions 
(Schenk 2001). 
Platform 
Figure 1. Coordinates and parameters involved in a LiDAR 
acquisition system 
X 
— Xq + R(x)j>icT >+ R(o<t>K R&a>Aj>AicR/3 
0 
0 
-P 
(1) 
1,2 Previous Researches 
In the past few years, several research methods for LiDAR 
calibration have been developed. For example, Schenk (2001) 
investigated the sources of systematic errors that can occur in a 
LiDAR system. A calibration procedure was then proposed 
using such an analysis. This work revealed that some of the 
calibration parameters cannot be easily estimated due to their 
strong correlation. The calibration methodology developed by 
Morin (2002) uses the LiDAR equation to solve for the bore 
sighting misalignment angles and the scanner angle correction. 
These parameters are either estimated using ground control 
points or by observing discrepancies between tie points in 
overlapping strips. However, the identification of distinct 
control and tie points in LiDAR data is a difficult task due to 
the irregular nature of the collected point cloud. To alleviate 
this difficulty, Skaloud and Lichti (2006) presented a 
calibration technique using tie planar patches in overlapping 
strips. The underlying assumption of this procedure is that 
systematic errors in the LiDAR system will lead to non 
coplanarity of conjugate planar patches as well as bending 
effects in these patches. The calibration process uses the 
LiDAR equation to simultaneously solve for the plane 
parameters as well as the bore-sighting misalignment angles. 
However, this approach requires having large planar patches, 
which might not always be available. In addition, systematic 
biases, which would not affect the coplanarity of conjugate 
planar patches, could still remain. The approaches taken by 
LiDAR surveying companies were more closely applicable in 
practice. For example, to calibrate the LiDAR system, Hanjin 
(2006) devised a calibration field which is composed of well- 
known surfaces. Using the calibration site, discrepancies 
between the LiDAR point cloud and the reference surface are 
observed and used to determine the system parameters such as 
the bore-sighting roll and pitch angles and scale parameters. 
The drawbacks of this approach are that the method involves 
manual and empirical procedures, and some parameters are not 
considered in the calibration procedure. 
This paper presents a new methodology for simultaneous 
estimation of the LiDAR bore-sighting parameters using control 
features that are automatically extracted from a reference 
control surface. In this approach, the reference control surface 
is derived from a terrestrial LiDAR system. The shorter ranges 
and the high point density associated with terrestrial LiDAR 
systems would ensure the generation of a reference surface, 
which is accurate enough for reliable estimation of the 
calibration parameters associated with airborne LiDAR systems. 
2. PROPOSED METHODS 
2.1 Point Primitives and ICP method 
The main issue considered in the proposed calibration 
procedure is identifying conjugate features in the airborne and 
terrestrial LiDAR systems. As mentioned above, due to the 
irregular nature of the generated point cloud from a LiDAR 
system, it is usually believed that there is no point-to-point 
correspondence between the derived points from the airborne 
and terrestrial systems. However, one might argue that point-to- 
point correspondences for a fraction of the terrestrial and 
airborne datasets can be assumed; considering the higher point 
density associated with terrestrial systems compared with that 
for airborne systems and the noise level in both datasets. 
Therefore, this paper introduces a point-based calibration 
procedure using pseudo-conjugate points in the terrestrial and 
airborne datasets, and Equation 2 shows the target function 
based on the point primitives for the calibration. In this 
Equation, the superscription T denotes the target data (airborne 
LiDAR data), while R denotes the reference data (Point cloud 
generated by terrestrial LiDAR system). While only point cloud 
coordinates are utilized from the reference data, LiDAR system 
raw measurements should be available for the target system. As 
shown in 
Figure 2, two points are used from both datasets, and 
points in reference data* 
points in calibration target data* 
Figure 2. The closest points selected by the iterative closest 
point procedure 
those points are selected by the iterative closest point (ICP) 
procedure (Zhang, 1994), which is one of the common surface 
matching method. After that, in the calibration procedure, two