The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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determine the boresight misalignment angles using overlapping 
LiDAR strips, flown in different directions. 
Not long ago, another rigorous class of calibration procedures 
started to emerge (Filin, 2003; Friess, 2006; Skaloud and Lichti, 
2006; Scaloud and Schaer, 2007). These types of approaches 
model all systematic errors directly in the measurement domain 
and condition groups of points to reside on a common surface 
of known form. 
The earlier methods, related to LiDAR strip adjustment, also 
addressed the effects of systematic errors in the registration 
(which was based on DEM matching) of overlapping point 
clouds. For extended literature review about co-registration, see 
in Pothou et al., 2006a; Pothou et al., 2006b. 
Currently the most common method of calibrating a LiDAR 
sensor is also the least rigorous: profiles of overlapping strips 
are compared and an experienced operator manually adjusts the 
misalignment angles until the strips appear to visually fit. 
Although practical, this approach is time consuming and labor 
intensive and the results do not immediately provide any 
statistical measure on the quality of the calibration (Morin and 
El-Sheimy, 2002). Furthermore, the existing methods often 
cannot reliably recover all three of the angular mounting 
parameters. The undetermined parameter(s) propagate into the 
subsequently captured data, therefore compromising the 
accuracy of any derived product. Thus, much research effort is 
still devoted to improve these processes. Most of the adopted 
approaches are usually based on either physical boundaries or 
cross-sections (Schenk, 2001) or DTM/DSM gradients (Burman, 
2000), mimicking the photogrammetric calibration approach via 
signalized or intensity-deduced targets points. The drawbacks 
of these methods is the lack (or simplification) of assurance 
measures, correlation with the unknown terrain shape or limits 
imposed by laser pointing accuracy and uncertainty due to 
beam-width. Habib et al. (2007), proposed a LiDAR system 
self-calibration using planar patches derived from 
photogrammetric data. Not only is the mathematical model for 
the LiDAR system calibration by using control planar patches 
presented but also the optimal configuration for flight 
conditions and the distribution of planar patches, to avoid 
possible correlations have also been analysed. 
Pothou et al. (2007), introduced a novel prototype algorithm for 
observing, and subsequently determining the boresight 
misalignment of LiDAR/IMU, using two different surfaces 
(point datasets). This algorithm minimizes the distances 
between points of the target surface and surface patches (TINs) 
of the reference surface, along the corresponding surface 
normals (based on Schenk et al., 2000). The technique can be 
applied to various data combinations, such as matching LiDAR 
strips or comparing LiDAR data to photogrammetrically 
derived surfaces. Object of simple shape similar to man-made 
structures, such as buildings, have been chosen and constructed 
to perform the surface matching. The processing algorithm 
includes additional testing of the validity, accuracy, and 
precision of various statistical tests (QA/QC - Quality 
Assurance/Quality Control) for outlier detection in positioning 
and attitude data. 
In this research, the feasibility of using urban areas for 
boresight misalignment is investigated. Buildings are of 
particular interest; in other words, what the impact of the 
building shape, size, distribution, etc. is on the performance of 
the boresight misalignment process. Photogrammetrically 
restituted buildings were used as reference surfaces called 
‘building-positions’ or ‘reference-positions’. The influence of 
the number and distribution of the necessary ‘building- 
positions’ on boresight’s misalignment parameter estimation is 
evaluated. Experiments with various number of ‘building- 
positions’ in regular as well as random distribution are 
presented, analyzed and evaluated through QA/QC statistical 
tests. The optimum number and distribution of ‘building- 
positions’ have been determined and proposed. 
In Section 2, a short review of the status of multi-sensor 
calibration and boresight misalignment of LiDAR/IMU is 
provided. Section 3 outlines the mathematical model of the 
algorithm for the boresight misalignment and presents the 
statistical analysis of the QA/QC techniques supported by the 
LiDAR/IMU boresight misalignment calculation. In Section 4, 
the dataset used for testing is described. The experimental 
results, as well as their statistical analysis and their effects on 
LiDAR points, are described in Section 5. Section 6 concludes 
the research. 
2. MULTI SENSOR CALIBRATION - BORESIGHT 
MISALIGNMENT 
The IMU frame is usually considered as the local reference 
system of the MMS system, and thus, the navigation solution is 
computed within this frame. The spatial relationship between 
the laser scanner and the IMU is defined by the offset and 
rotation between the two systems. To obtain the local object 
coordinates of a LiDAR point, the laser range vector has to be 
reduced to the IMU system by applying the offsets and rotations 
between the two systems, which provides the coordinates of the 
LiDAR point in the IMU system. The GPS/IMU based 
navigation provides the orientation of the IMU frame, including 
position and attitude, and thus, the mapping frame coordinates 
can be subsequently derived. In our discussion, the 
determination of the boresight offset (b x , b y , b z ) and the 
boresight matrix (rotations co, tp, k) between the IMU and the 
laser frame (provided that sufficient ground control is available) 
is addressed. 
Any discrepancy in boresight values results in a misfit between 
the LiDAR points and the ground surface, and thus, the 
calculated coordinates of the LiDAR points are not correct 
(Toth, 2002). Ideally, the calibration parameters should stay 
constant for subsequent missions. The description of the effects 
of the different boresight misalignment angles is omitted here; 
for details see (Baltsavias, 1999). For a detailed description of 
multisensor calibration - boresight misalignment, see Toth and 
Csanyi, 2001; Toth, 2002; Pothou et al., 2007. 
3. MATHEMATICAL MODEL OF THE ALGORITHM 
Two datasets, called point clouds, P (x pi , y pi , z^) (p f = 1,..., n) 
and Q (Xqj, y qi , z qi ) (qj= 1,..., m), which describe the same 
object are captured by different technologies and they must be 
transformed into a common system. Assuming that these 
datasets are connected by a 6-parameter 3D transformation, the 
three offset and three rotation parameters can be estimated, 
minimizing the distance between a point of Q dataset and a TIN 
surface patch of P surface, which is described by points of P 
dataset (Equation 1). In Figure 1, point qj (Xqj, y qi , z qi ) of Q 
point cloud, has to be transformed to the closest surface patch 
of the control surface P, defined by 3 points (p m , p k , pQ, through