The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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Figure 3. Setting of sensors 
3. MULTI SENSOR INTEGRATION 
3.1 Overview of Sensor Integration 
Figure 4 shows the overview of data processing. In this research, 
following data are acquired; base station GPS data, remote 
station GPS data, IMU data, CCD images, and laser range data. 
Though the data are acquired in different frequencies, they are 
synchronized each other by GPS time. 
GPS data. Image overlap is estimated by GPS altitude 
information. Then, image matching of overlapped area is 
conducted and mosaic image is constructed. Mosaic images can 
cover more wide even though flight altitude is no so high. Fish- 
eye lens image is also acquired from the UAV to cover wide 
area as shown in Figure 7. The ground coverage from 50m 
altitude is 330m X 110m. 
Figure 5. Multi camera setting 
Coordinate Conversion 
i 
Construction of DSM 
Figure 6. CCD Images by multi cameras 
Figure 4. Sensor integration 
At first, differential GPS post processing is conducted by 
Waypoint’s GrafNav commercial software with accuracy of 
approximately 30cm. Secondly, processed GPS data and IMU 
data are integrated by Kalman filter operation. Thirdly, bundle 
block adjustment of CCD images is made with the support of 
GPS/IMU. Finally, GPS/IMU and the result of bundle block 
adjustment are combined to generate high precision and time 
series position and attitude. Then, this hybrid positioning data is 
used for coordinate conversion of laser range data and 
construction of digital surface model. 
3.2 Multi Cameras 
In order to cover wide area from low altitude, multi cameras are 
mounted on the UAV. In this study, two digital cameras and 
two IR cameras are mounted in parallel with some angles. 
These CCD cameras are tightly fixed on the iron plate to have 
constant geometric relationship during experiment as shown in 
Figure 5. 
Four images are acquired at the same time, as shown in Figure 6. 
Those images are mosaicked by using calibration data and 
result of image orientation. Rectified images are mosaicked by 
Figure 7. Fish-eye lens image 
3.3 GPS/IMU integration by Kalman filter 
The integration of GPS and IMU is implemented by using 
Kalman filter. The Kalman filter can be used to optimally 
estimate the system states. In the Kalman filter, the final 
estimation is based on a combination of prediction and actual 
measurement. The pure navigation algorithm is operated for 
IMU to calculate attitude, velocity, and position (Kumagai, et 
al., 2002). The navigation procedure proceeds step by step. 
Inertial navigation starts to define its initial attitude and heading. 
Then, its process changes to the navigation mode. IMU has a