The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B5. Beijing 2008 
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The MCS software runs on the ground base station computer 
and manages the guidance and navigation control behavior of 
the UAV system (Raunaq et al., 2007). 
The Rotomotion AFCS consists of an embedded computer 
running Linux, a WAAS-enabled GPS unit, three 
accelerometers, three gyroscopes, and a three-axis 
magnetometer. It utilizes PID controllers to maintain attitude 
and altitude in translational flight and hover as well as flight 
during a fast forward flight mode. The GPS unit is primarily 
used to provide position of the aircraft and maintain course and 
speed as well as fixed hovering positions. 
The AFCS performs the attitude and position control of the 
UAV. It maintains the stability of the helicopter in hover and 
translational flight. The UAV will perform an autonomous 
translational maneuver only when the AFCS is sent a waypoint 
from the MCS. The AFCS computer can store and execute a 
way-point stack, allowing the helicopter to follow a pre 
programmed path even if it is outside of radio range or line-of- 
sight. This option is only utilized during specific flight tests or 
in situations when the MCS is unavailable. As a safety 
precaution, the AFCS will be sent only one waypoint at a time 
by the MCS allowing a mission to be halted immediately at any 
point. In the event that the communication between the AFCS 
and MCS is broken, the AFCS will stop and hover the 
helicopter at the most recently received waypoint. 
3.3 Imaging Sensor 
We currently use a UEye 2220c USB video camera with frame 
size 768x576. There is also OptiLogic RS-232 laser range 
finder on board. The UEye camera can provide RGB nature 
color images. The camera also supports NEMA sentence 
capturing from external GPS units, thus the time of image 
capture and the exact position of capture could be recorded into 
the image header for later review and correlation. A custom 
camera trigger is made controller by the AFCS. The camera 
will be triggered at preset GPS waypoints. 
4. INITIAL DATA COLLECTION AND PRELIMINARY 
RESULTS 
The first data collection using the above UAV system was 
conducted in a test site near Rapid City, South Dakota. The 
UAV flew at an altitude of about 50m above ground, capturing 
details of the road surface. Figure 3 is an example of the road 
imagery collected over a road segment with corrugation 
(washboarding). Ground shots presented in Figure 3a and 
Figure 3b indicate the very mild washboarding defects with 
depth around one inch. However, these washboarding features 
are clearly visible in the UAV image presented in Figure 3c. 
Figure 4 is an image of a road segment with more serious 
distress affecting the entire road cross-section geometry. An 
overview of this segment is given in the upper part of the Fig 4. 
The initial low resolution shot of the overall cross section 
reveals the potential to evaluate potholes, rutting, loss of 
aggregate cover, drainage issues, and the poor overall cross 
section. The imagery was acquired early in the morning right 
after sunrise. Large shadows of trees clearly appear in the 
image. The details of the distresses are demonstrated in lower 
part of the figure. 
Figure 3. Road segment with moderate corrugation as seen 
from ground image (top), measurement of the depth of 
corrugation (middle) and UAV Image (bottom). 
We are developing image processing algorithms to detect and 
extract road defects from the UAV-collected imagery. The 
development includes detection of various road distresses from 
2D imagery, analysis of the inherent 3D geometry information 
in images using photogrammetric techniques, and the fusion of 
2D and 3D information to derive road condition parameters. 
The current 2D image analysis uses image features (such as 
color, edges etc.), pattern recognition and image classification 
techniques, and is powerful as demonstrated in the following 
example.