The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008
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the coordinate of CCD images. Texture data are overlaid on
geo-referenced point cloud data. The integrated point cloud data
shows a good matching with image data because hybrid IMU
data is initialized by the result of bundle block adjustment of
sequential image data. Each point of geo-referenced laser point
data corresponds to a pixel of geo-referenced CCD image in the
same coordinate system. In this research, 3D point cloud data
takes on a color from the corresponding image pixels for
textured DSM as shown in Figure 13. Figure 14 is ortho mosaic
image which is constructed by using digital surface model and
oriented images.
Figure 16. 3D point cloud data with NDVI
Figure 13. DSM
Figure 14. Ortho mosaic image
4.3 Vegetation Index
As the same as digital camera image, image processing is
conducted for IR camera images as shown in Figure 15. After
image orientation calculated in IR images, NDVI is calculated.
Laser range data and IR camera images corresponds each other.
Then, vegetation index is applied to digital surface model. 3D
point cloud data takes on a color from the corresponding NDVI
images. Figure 16 shows 3D point cloud data with NDVI.
4.4 Accuracy assessment
In this study, multi sensors are integrated for mapping, so it is
difficult to verify the origin of errors. Therefore, the accuracy
assessment of DSM is done by comparing with control points
from oriented images. Control points are selected 10 feature
points such as object comers or conspicuous feature. As a result,
average error of digital surface model is approximately 10cm to
30cm as shown in Table 3. The accuracy of the hybrid IMU is
also estimated by the this mapping accuracy.
Control point from Image
Dai DSM
D3VI
Error
Unit:m
Bror Error
No.
(X) (Y)
(ZD
(X) (Y>
(Z)
OC
(Y>
(Z>
1
-11184.877-25630.253
42.755
-11184.696-25630.836
42.915
0.181
0.583
0.160
2
-11185.471 -25622.727
42.952
-11185.557-25622.789
42.971
0.086
0.062
0.019
3
-11167.603-25670.474
42.391
-11168.282-25670.312
42.406
0.679
0.162
0.015
4
-11177.107-25634.721
42.704
-11177.262-25634.918
42.523
0.155
0.197
0.181
5
-11152.866-25641.753
42.029
-11152.172-25641.036
42.071
0.694
0.717
0.042
6
-11176.511-25625.571
42.824
-11176.467-25625.426
42.767
0.044
0.145
0.057
7
-11153.911-25643.823
42.534
-11154.375-25643.041
42.075
0.464
0.782
0.459
8
-11150.564-25631.724
42.340
-11150.887-25631.869
42.296
0.323
0.145
0.044
9
-11176.771 -25635.344
43.992
-11176.394-25635.308
44.082
0.377
0.036
0.090
10
-11186.666-25631.657
44.289
-11186.417-25631.888
44.202
0.249
0.231
0.087
Ave.&ror 0.325
0.306
0.115
T
able 3. Accuracy of digital surface model
5. CONCLUSION
In conclusion, all the inexpensive sensors, laser scanner, CCD
cameras, IMU, and GPS are integrated for mapping. Digital
surface model and ortho mosaic images are constructed by
using all the sensors. In this research, a new method of direct
geo-referencing for laser range data and CCD images by the
combination of GPS/IMU and bundle block adjustment by the
Kalman filter is proposed. Because of the aiding accumulation
of error of the Kalman filter by bundle block adjustment, geo-
referenced laser range data and CCD images are overlapped
correctly in the common world coordinate system automatically.
The data resolution and accuracy of mapping is good enough
compared with satellite remote sensing and aerial remote
sensing. All the sensors and equipments are assembled and
mounted on an UAV in this experiment. This paper focus on
how integrate these sensors with mobile platform. Finally,
precise trajectory of the sensors is computed as hybrid IMU and
it is used to construct digital surface model with texture and
vegetation index. In the future, small UAV system is also
applied with selected sensors for certain observation target.
References from Journals:
Nagai, M., Shibasaki, R., 2006, Robust Trajectory Tracking by
Combining GPS/IMU and Continual CCD Images, Proceedings
