Mapping Road surface condition using Unmanned Aerial Vehicle- Based Imaging System. Ahmed F. Elaksher St. Cloud State University

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1 Mapping Road surface condition using Unmanned Aerial Vehicle- Based Imaging System Ahmed F. Elaksher St. Cloud State University 1

2 Outline Introduction & Motivation Methodology Experimental Results & Analysis 2

3 Introduction & Motivation 3

4 UAV: Unmanned Aerial Vehicle Scan Eagle Weight: 40-pounds Travel more than 15 hours Cruising at speeds 60 mph Altitude 5000m. EMT Aladin Weight: 6 pounds Travel about 60 minutes Cruising speed 100 mps Altitude: m RQ-4 Global Hawk Weight: 8490 pounds Travel more than 36 hours Cruising at speeds 404 mph Altitude 19928m. Source: 4

5 Advantages of UAV in remote sensing UAVs are highly flexible source for remote sensing data. UAVs can be programmed off-line and controlled in real time to navigate and to collect data. UAVs are able to operate rather close to the object, acquiring image with resolution as fine as a few centimeters UAVs have other advantages over satellites and manned aircraft, such as collecting image data at a lower cost, faster and more safely. 5

6 Applications of UAV in remote sensing mapping urban and suburban areas (Spatalas et. al, 2006) fire monitoring (Zhou, 2005) road following (Egbert, 2007) modeling of archaeological sites (Pueschel et. al, 2008; Eisenbeiss, 2004;)

7 UAV mapping system 7

8 Remote control ground control station 8

9 The helicopter: weights 15lb, can reach 200m, maximum speed of 10m per second. GPS receiver: provides the 3D coordinates information Inertial Navigation System: provides the orientation and velocity of the UAV. Autopilot board: acquires GPS & inertial navigation system data, and control the flight & camera. The Autopilot board can be controlled with manual remote control and can be engaged/disengaged upon the command. Camera: Canon EOS Digital Rebel XTi digital camera, f=50mm, 3888x2592pixels, pixel size of 5.7µm Ground Control: The wegcs software is installed on the GCS computer. The software features an interface for mission planning allowing for setting of mission parameters. 9

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11 Methodology 11

12 UAV-acquired Imagery Camera Calibration Parameters Iteratively Forming Image Block Select one pair of stereo images and perform relative orientation Generate/update 3D model coordinates Add neighboring overlapping image and perform space resection Bundle adjustment Bundle block adjustment 3D point coordinates 12

13 Camera calibration The aim of camera calibration is to calculate the so-called inner orientation parameters (focal length, lens distortion, ). Color-coded targets are used and In this research, the camera calibration is performed via the iwitness scooftware. 13

14 UAV-acquired Imagery

15 Test site 15

16 Stereo images relative orientation Location of conjugate points across images Point matching by comparing of attributes SIFT algorithm for feature point extraction

17 Determine the relative orientation of the two images. This serves the starting point of the whole image network

18 Generate 3D model coordinates Add the neighboring images, iteratively to the image block Perform bundle adjustment of the formed image network to simultaneously determine the orientation parameters of the all images

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20 3D point coordinates An efficient approach to process the UAV-acquired imagery to derive 3D road surface fully automatically. Since a point on a road is captured in consecutive images, thus, by reversing the imaging process, its 3D position can be computed through the intersection of the image rays. The fundamental process to automate this procedure is to locate the corresponding points in image space. This is achieved by automated image matching. 20

21 Experimental Results & Analysis 21

22 Research and Development 22

23 Results 23

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26 Analysis The depth of the potholes is around 1.5~2 inches measured in field with tape. The maximum depths of the ruts and potholes are 1.5 ~2. Comparison with field survey with tape was also conducted. The differences between image-based measurement and field survey are within the range of , demonstrating good performance of the system. 26

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