The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
2. IMU body frame 
Figure 1. Coordinate systems and involved quantities in the LiDAR equation 
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footprints are provided. Therefore, methods for adjusting 
LiDAR strips, which are only based on the XYZ-coordinates, 
are required. 
In the past few years, several methods have been developed for 
evaluating and/or improving LiDAR data quality by checking 
the compatibility of LiDAR footprints in overlapping strips 
(Kalian et al., 1996; Crombaghs et al., 2000; Maas, 2000; Bretar 
et al., 2004; Vosselman, 2004; Pfeifer et al., 2005). In 
Crombaghs et al. (2000), a method for reducing vertical 
discrepancies between overlapping strips is proposed. This 
approach does not deal with planimetric discrepancies, which 
might have larger magnitude when compared with vertical 
discrepancies. In Kilian et al. (1996), an adjustment procedure 
similar to the photogrammetric strip adjustment was introduced 
for detecting discrepancies and improving the compatibility 
between overlapping strips. The drawback of this approach is 
relying on distinct points to relate overlapping LiDAR strips 
and control surfaces. Due to the irregular nature of the LiDAR 
footprints, the identification of distinct points (e.g., building 
comers) is quite difficult and not reliable. More suitable 
primitives have been suggested by Maas (2000), where the 
correspondence is established between discrete points in one 
LiDAR strip and TIN patches in the other one. The 
correspondences are derived through a least-squares matching 
procedure where normal distances between conjugate point- 
patch pairs are minimized. This work focused on matching 
conjugate surface elements rather than improving the 
compatibility between neighbouring strips. Bretar et al., (2004) 
proposed an alternative methodology for improving the quality 
of LiDAR data using derived surfaces from photogrammetric 
procedures. The main disadvantage, which limits the 
practicality of this methodology, is relying on having aerial 
imagery over the same area. In Pfeifer et al. (2005) and 
Vosselman (2004), other methods were developed for detecting 
discrepancies between overlapping strips. 
The main objective of this paper is to present a new procedure 
for the strip adjustment while utilizing appropriate primitives 
that can be extracted from the LiDAR data with a satisfactory 
level of automation (i.e., requiring minimum user interaction). 
The paper starts with a brief discussion of the LiDAR error 
budget. Then, the proposed procedure for the strip adjustment, 
including the extraction and matching of the appropriate 
primitives, is presented. The performance of the proposed strip 
adjustment procedure is evaluated through experimental results 
from real data. Finally, the paper presents some conclusions and 
recommendations for future work. 
2. LiDAR ERROR BUDGET 
The quality of the derived point cloud from a LiDAR system 
depends on the random and systematic errors in the system 
measurements and parameters. A detailed description of LiDAR 
random and systematic errors can be found in Huising and 
Pereira (1998), Baltsavias (1999), and Schenk (2001). The 
magnitude of the random errors depends on the accuracy of the 
system’s measurements, which include position and orientation 
measurements from the GPS/IMU, mirror angles, and ranges. 
Systematic errors, on the other hand, are mainly caused by 
biases in the bore-sighting parameters relating the system 
components as well as biases in the system measurements (e.g., 
ranges and mirror angles). As a strip adjustment procedure is 
concerned with minimizing the impact of systematic errors in 
the LiDAR system on the derived point cloud, it is mandatory 
to understand the nature and impact of possible systematic 
errors in a LiDAR system. 
In this work, a simulation process was carried out to analyse the 
impact of systematic errors/biases in the bore-sighting 
parameters (spatial and rotational) on the derived point cloud. 
The process starts from a given simulated surface and trajectory, 
which are then used to derive the system measurements (ranges, 
mirror angles, position and orientation information for each 
pulse). Then, biases are added to the system parameters, which 
are used to reconstruct the surface through the LiDAR equation. 
The differences between the bias-contaminated and true 
coordinates of the footprints within the mapped area are used to 
represent the impact of a given bias in the system parameters or 
measurements. Due to the presence of systematic errors in the 
system parameters, the bias-contaminated coordinates of