Lines Location 
J Stnp 1 
Strip 2 
; Strip 3 
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
Using the semi-automated procedure described in section 4.1, 
we extracted conjugate lines in the three overlapping strips 
(Figure 6). Table 2 shows the estimated transformation 
parameters. As it can be seen in this table, there is a significant 
discrepancy between conjugate lines in overlapping strips 
especially in the X direction. Such discrepancies indicate the 
presence of biases in the 
measurements. 
system 
parameters 
and/or 
Strips 
Strips 
Strips 
1&2 
2&3 
1&3 
Transf. Parameters/ # of lines 
14 
11 
13 
X T (m) 
0.39 
0.72 
-0.09 
Y T (m) 
0.06 
-0.17 
0.21 
Z T (m) 
0.01 
0.01 
-0.14 
D(°) 
-0.0174 
-0.0300 
0.0119 
0(°) 
-0.0096 
-0.0093 
0.0011 
K(°) 
0.0027 
-0.0133 
-0.0212 
RMSE (m) 
0.12 
0.15 
0.20 
Table 2. Estimated transformation parameters using conjugate 
linear features in overlapping strips together with the RMSE 
after applying the transformation 
Figure 6. Extracted lines in the three overlapping strips 
The noise level was evaluated by computing the average normal 
distance between conjugate linear features after applying the 
estimated transformation parameters. The results are reported in 
Table 2. 
6. CONCLUSIONS 
This paper has outlined a new tool for the evaluation of LiDAR 
data quality. The paper started with a brief analysis of random 
and systematic errors in the system measurements and 
parameters and their impact on the derived point cloud 
coordinates. From this analysis some conclusions regarding the 
mathematical relationship between conjugate surface elements 
in overlapping strips could be drawn. It was concluded that a 
rigid body transformation is an appropriate model for relating 
conjugate points in the presence of the biases in the bore 
sighting parameters (spatial and rotational). Following such an 
analysis, the paper introduced a quality control procedure based 
on the use of linear features, which can be used to check for the 
presence of systematic errors in the data acquisition system as 
well as evaluating the noise level in the delivered point cloud. 
The proposed procedure has a satisfactory level of automation 
requiring minimal interaction from the operator (just a few 
clicks on the intensity image). The results from the real data 
have shown that collected LiDAR data might exhibit significant 
incompatibilities due to insufficient calibration procedures. 
Future research will focus on relating the detected discrepancies 
between overlapping strips to the system biases. Moreover, we 
will be using the estimated transformation parameters to 
remove the bias effect from the point cloud. In addition, the 
estimated system biases will be compared to those derived from 
rigorous calibration procedures. The presented models for linear 
scanner will be also expanded to include elliptical LiDAR 
systems. Also, we will be developing some standards and 
specifications that will allow for the acceptance or rejection of 
delivered point cloud to the end user. With the wide spread 
adoption of LiDAR systems for topographic data acquisition, 
we believe that this research is critical to strengthen the users’ 
confidence in the delivered point cloud especially in the 
absence of traditional measures, which are provided by other 
mapping techniques. 
ACKNOWLEDGEMENT 
We would like to thank the GEOIDE (GEOmatics for Informed 
DEcisions) Network of Centers of Excellence of Canada and 
the BMGS (Base Mapping and Geomatic Services) for their 
partial financial support of this research. The authors are also 
indebted to LACTEC - Institute of Technology for the 
Development - for providing the LIDAR data and the valuable 
feedback. 
REFERENCES 
Baltsavias, E., 1999. Airborne laser scanning: existing systems 
and firms and other resources, ISPRS Journal of 
Photogrammetry and Remote Sensing, 54 (2-3), pp. 164-198. 
Bretar F., M. Pierrot-Deseilligny, and M. Roux, 2004. Solving 
the Strip Adjustment Problem of 3D Airborne Lidar Data. 
Proceedings of the IEEE IGARSS’04, 20-24 September, 
Anchorage, Alaska. 
Crombaghs, M., E. De Min, and R. Bruegelmann, 2000. On the 
Adjustment of Overlapping Strips of Laser Altimeter Height 
Data. International Archives of Photogrammetry and Remote 
Sensing, 33(B3/1), pp. 230-237. 
El-Sheimy, N., C. Valeo, and A. Habib, 2005. Digital Terrain 
Modeling: Acquisition, Manipulation And Applications, 
Artech House Remote Sensing Library, 257 p. 
Filin, S., 2003. Recovery of systematic biases in laser altimetry 
data using natural surfaces. Photogrammetric Engineering 
and Remote Sensing, 69(11), pp. 1235-1242. 
Huising, E. J., and L. M. G. Pereira, 1998. Errors and Accuracy 
Estimates of Laser Data Acquired by various Laser Scanning 
Systems for Topographic Applications, ISPRS J. of 
Photogrammetry and Remote Sensing, 53(5), pp. 245-261. 
Kilian, J., N. Haala, and M. Englich (1996). Capture and 
evaluation of airborne laser scanner data. International 
208