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.
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