1

Mapping Road surface condition

using Unmanned Aerial Vehicle-

Based Imaging System

Ahmed F. Elaksher

St. Cloud State University

2

Outline

• Introduction & Motivation

• Methodology

• Experimental Results & Analysis

3

Introduction & Motivation

4

Scan Eagle

Weight: 40-pounds

Travel more than 15 hours

Cruising at speeds 60 mph

Altitude 5000m.

RQ-4 Global Hawk

Weight: 8490 pounds

Travel more than 36 hours

Cruising at speeds 404 mph

Altitude 19928m.

UAV: Unmanned Aerial Vehicle

Source: https://www.e-education.psu.edu/geog883/l8_p8.html

EMT Aladin

Weight: 6 pounds

Travel about 60 minutes

Cruising speed 100 mps

Altitude: 100-300m

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.

Applications of UAV in remote sensing

modeling of archaeological

sites (Pueschel et. al, 2008;

Eisenbeiss, 2004;)

fire monitoring (Zhou, 2005)

road following (Egbert, 2007)

mapping urban and suburban areas

(Spatalas et. al, 2006)

7

UAV mapping system

8

ground control station

Remote control

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.

10

11

Methodology

12

Bundle block adjustment

3D point coordinates

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

UAV-acquired Imagery Camera Calibration Parameters

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.

UAV-acquired Imagery

15

Test site

15

Stereo images relative orientation • Location of conjugate points across images

SIFT algorithm for feature point extraction

Point matching by comparing of attributes

• Determine the relative orientation of the two images.

This serves the starting point of the whole image

network

• 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

Generate 3D model coordinates

19

20

•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.

3D point coordinates

21

Experimental Results & Analysis

22

Research and Development

23

Results

24

25

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 0.2”-0.4”, demonstrating good

performance of the system.

27