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INTEGRATION OF TERRESTRIAL AND AIRBORNE LIDAR DATA FOR SYSTEM 
CALIBRATION 
Ki In Bang 3, *, Ayman F. Habib 3 , Kresimir Kusevic b , Paul Mrstik b 
a Department of Geomatics Engineering, University of Calgary, Canada - (habib, kibang)@ucalgary.ca 
b Terrapoint Inc., Canada - (kresimir.kusevic, paul.mrstik)@terrapoint.com 
Commission I, WG 1/2 
KEY WORDS: LiDAR, Calibration, Quality Assurance, System Biases, Areal Features, Surface Matching 
ABSTRACT: 
The ever improving capabilities of the direct geo-referencing technology is having a positive impact on the widespread adoption of 
LiDAR systems for the acquisition of dense and accurate surface models over extended areas. LiDAR systems can quickly provide 
accurate surface models with a dense set of irregular points, surpassing the quality of those derived from other techniques, such as 
manual photogrammetric DSM generation, radar interferometry, and contour interpolation. A typical LiDAR system consists of 
three main components: a GNSS to provide position information, an INS for attitude determination, and a laser scanner to provide 
the range/distance from the laser-beam firing point to its footprint. The accuracy of the LiDAR point cloud is ensured by the quality 
of the measurements from the individual system components and their spatial relationship as defined by the bore-sighting parameters. 
Even though the measurements of the individual system components (GNSS, INS and laser scanner) are quite precise, serious errors 
can result from inaccurate estimation of the bore-sighting parameters. For this reason, bore-sighting parameters should be well 
defined at the beginning of the work process and will be the focus of this paper. 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. After 
introducing the mathematical models for the proposed methodologies, this paper outlines the optimal configuration of the control 
data for a reliable estimation of the calibration parameters, while avoiding possible correlations among these parameters. Finally, the 
feasibility test presents experimental results from real datasets while highlighting the advantages and the limitations of the proposed 
methodologies. 
1. INTRODUCTION 
1.1 Basics of a LiDAR System 
Recently, LiDAR systems have been proven as a cost-effective 
tool for the generation of surface models over extended areas. 
They can quickly provide accurate surface models with a dense 
set of irregular points, surpassing the quality of those derived 
from other techniques, such as manual photogrammetric DSM 
generation, radar interferometry, and contour interpolation. A 
typical LiDAR system consists of three main components, a 
GNSS system to provide position information, an INS unit for 
attitude determination, and a laser system to provide range 
(distance) information between the laser firing point and the 
ground point. In addition to range data, modem LiDAR systems 
can capture intensity images over the mapped area. Therefore, 
LiDAR is being more extensively used in mapping and GIS 
applications. 
Figure 1 shows a schematic diagram of a LiDAR system 
together with the involved coordinate systems. Equation 1 is the 
basic LiDAR geometric model that incorporates the LiDAR 
measurements for deriving positional information. This 
equation relates four coordinate systems, which include the 
ground coordinate system, the inertial measurement unit (IMU) 
body frame coordinate system, the laser unit coordinate system, 
and the laser beam coordinate system. This equation is simply 
the result of a three vector summation; ' s the vector from 
the origin of the ground coordinate system to the IMU body 
frame, P is the offset between the laser unit and the GNSS 
phase center with respect to IMU body frame, and p is the 
vector between the laser beam firing point and the object point, 
which is defined in the laser beam frame. The summation of 
these three vectors after applying the appropriate rotations 
(RINS, > ^scan ) will yield the vector A , which represents the 
ground coordinates of the object point under consideration. The 
quality of the derived surface depends on the accuracy of the 
involved sub-systems (i.e., laser, GNSS, and INS) and the 
calibration parameters relating these components (i.e., bore 
sighting parameters). 
Even though the individual measurement capabilities of the 
system components (GNSS, INS and laser scanner system) are 
quite precise, serious errors can occur from inaccurate 
combination of theses components. For this reason, bore 
sighting parameters should be well calibrated before surveying 
missions. The ultimate goal of the LiDAR system calibration is 
to determine all systematic parameters involved in a LiDAR 
equation and to obtain the correct raw measurements. The 
Corresponding author