Indian Journal of Marine Sciences

Vol. 38(3), September 2009, pp. 324-331

Vision based distance measurement system using single laser pointer

design for underwater vehicle

Muljowidodo K1, Mochammad A Rasyid

2, SaptoAdi N

2 & Agus Budiyono

3*

1 Mechanical Engineering Program, Mechanical Engineering and Aeronautics Faculty, InstitutTeknologi Bandung (ITB)

Ganesha 10, Bandung, West Java, Indonesia

[E-mail: muljo@bdg.centrin.net.id] 2 Center for Unmanned System Studies (CentrUMS), InstitutTeknologi Bandung (ITB), Ganesha 10, Bandung, West Java, Indonesia

[E-mail: aminvsky@yahoo.com, sapto131@students.itb.ac.id] 3 Corresponding Author, Department of Aerospace Information, Smart Robot Center, Konkuk University

1 Hwayang-Dong, Seoul 143-701, Korea

[E-mail: budiyono@alum.mit.edu]

Received 26 July 2009, revised 11 September 2009

As part of a continuous research and development of underwater robotics technology at ITB, a vision- based distance

measurement system for an Unmanned Underwater vehicle (UUV) has been designed. The proposed system can be used to

predict horizontal distance between underwater vehicle and wall in front of vehicle. At the same time, it can be used to

predict vertical distance between vehicle and the surface below it as well. A camera and a single laser pointer are used to

obtain data needed by our algorithm. The vision-based navigation consists of two main processes which are the detection of

a laser spot using image processing and the calculation of the distance based on laser spot position on the image.

[Keywords: Vision Based, Image Processing, Laser Spot]

Introduction Design and development of several UUV

prototypes have been conducted elsewhere1. Vision

based navigation has been investigated and an

approach by using single laser pointer is presented in

this paper. UUV is usually equipped with camera as

the “eye” of the operator. On other hand, camera

supported by computer vision can also give some

important information. In this paper it is proposed the

design of system and algorithm to be used for

calculating horizontal and vertical distance between

an object and camera. Beside the camera, a laser

pointer is used for the setup. It is assumed that a

standard computer is used for image processing and

data calculation. Typical underwater vehicle platform

installed with camera and laser pointer is depicted

in Fig. 1.

There are two major works in designing this

distance measurement system. First is obtaining a real

time image processing algorithm needed for laser

spot/mark detection. Second is finding a scaling factor

or formula that convert the object position (pixels) on

the image into real world position (meters). The paper

comprises the relevant aspects of image processing

requirement, image processing algorithm, camera

mounting, laser pointer mounting, detail calculation

of distance measurement and the experimental results.

_____________

*Author for Correspondence

Fig. 1—Underwater vehicle platform with camera and laser

pointer

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Materials and Methods

Image processing requirement

In our system, we need appearance of object on the

image captured by video camera. Based on the

detected object one can measure the relative distance

between object and the camera. To be implemented in

real time, one must be able to process the images with

a frequency of at least 25 frames per second.

There are many well-known algorithms such as

MSER2, SIFT

3, etc. to be used for object detection

and recognition. The main problem with those

algorithms is the processing time. It takes few

hundreds milliseconds up to few seconds for complex

image per frame. Thus, it is difficult to be used as part

of a real time control system. Considering this

limitation, it is necessary to develop as simple image

processing algorithm as possible.

It is assumed that a laser pointer can produce a

salient spot. This saliency can simplify the detection

and recognition of image processing task. It reduces

processing time substantially. In our proposed

technique, we just use a laser pointer instead of two

lasers as proposed in elsewhere4.

Image processing algorithm

A red laser pointer is used for experiment. The light

spot of the laser is object to detect. It is a relatively

simple object. There was no need of a complex

feature extraction algorithm. It required a red

segmentation or separation as main task filtering in

our image processing algorithm. Then, by applying a

simple object center finding we can obtain the

position of the detected object. The algorithms for

color segmentation or separation have been

introduced in many papers5. The present study also

presents implementations for a traffic sign

recognition. In the past, our red segmentation was

based on HSV (Hue, Saturation, Value) color space.

This is a standard method for color segmentation.

Color can be represented by only hue components.

Moreover, hue is invariant to the variations in light.

This is the important feature of hue. We used only hue

colors, while others use hue-saturation color. Red lies

in certain range value in hue color space. By applying

a simple thresholding, red can be separated from other

colors based on that range value. One should define

the range so that our intended red object can be

thresholded as distinct as possible. The main

drawback of this algorithm is that we have to set up a

good range value of red. It is not easy to have a good

value since a certain range of values could work in

some situation but fail in others.

An algorithm presented in Flegh5

et al offers a

solution to overcome this dynamic behavior. It is

called dynamic threshold algorithm. It takes into

consideration the variation of global color of the

image. The range value of red used for thresholding is

influenced by this global value. In their experiment, a

red segmentation based on this algorithm yields a

good result. Whereas, in our system we cannot

achieve a real time processing using that algorithm.

It is proposed a simple red segmentation with no

value setting. Our algorithm converts RGB color

space into a single component color space which

represents the degree of red. The redder the color of a

pixel in RGB color space, the higher degree of red it

will have. Let Ri, Gi, Bi be the Red, Green, Blue

values of a pixel. Let DRi be the degree of red of a

pixel. For all pixel then:

Ri = Ri - (Gi+Bi)

Gi = Ri - Gi

Bi = Ri - Bi

If (Ri<0) Ri = 0

If (Gi<0) Gi = 0

If (Bi<0) Gi = 0

DRi = Ri + Gi + Bi

These formula try to measure the degree of red

compared to other colors. The red degree of each

RGB component is calculated as presented by the

formula. Finally, the degrees are totalized to obtain

degree of red of a pixel. Fig. 2 is an example of the

algorithm implementation applied on a laser spot.

Beside the algorithm we use some filtering, such as

smoothing and dilating for image conditioning. Fig. 3

is a diagram of part of the program :Input RGB

image. Smoothing is needed to eliminate the effect of

camera noise. RGB to Degree of Rred Converting has

been presented above. Degree of Red Thresholding is

Fig. 2—RGB to degree of red conversion

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to convert degree of red image into black and white

image. Dilating is needed to combine two or more

very close white objects since they could come from a

single object. Component Connecting is to build

connected components exist on thresholded image.

Object Finding is based on two connected

components which have the highest degree of red

value. The fact that the laser spot lies at the center of

horizontal axis will be used to get the correct object.

Camera mounting Camera is mounted inside the UUV tube. Medium

inside the tube is air. The object is in the sea water.

Because of difference between these mediums, light

coming from object to the camera could be refracted.

This can cause image distortion. It is not critical for

our eyes, but it does make distance calculation more

complex.

To avoid this image distortion, achieve a simpler

calculation and get true proportion image, the setup of

camera mounting must follow these two conditions:

1 The camera lens must be mounted at the center of

the glass dome.

2 If the camera intended to have tilting capability,

the tilt axis of the camera must be at the center of

the glass dome. For our setup, we just fix the

camera, no tilting.

It is assumed that water sea refraction index near

the UUV is homogenous meaning no light refraction

exists in the water. In Fig. 4, each beam of light is

perpendicular to surface of the glass dome so that

there is no light refraction yielded. Since the camera

will now see the object as if it is in the air, one can

adopt any formula that works in the air without any

additional correction. This simplifies our works

substantially.

Laser pointer mounting

Laser pointer can be mounted inside the UV tube,

behind the glass dome, or outside the UV tube. We

found several difficulties in the first alternative. If a

Fig. 3—Image processing flowchart of red object detection

Fig. 4—Camera mounting setup

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laser pointer is mounted as shown in Fig. 5, the

following problems arise:

1 Laser beams hitting the dome wall will create

spots at the wall. These spots will be captured by

camera and detected by algorithm as objects

although these are not the true objects.

2 Laser beams are refracted since they are not

perpendicular to the dome surface. We don’t have

refraction index sensor of sea water installed on

our UV. Moreover, the distance calculation will

be much complex.

Other typical setup is mounting the laser pointers

outside the dome as shown in Fig. 6, so no refraction

index needed. The laser pointer axis and the camera

axis are all parallel. Distance can be measured by

associating the distance (in pixels) between two spots

on the image to the real world distance using a simple

linear scaling formula. The drawbacks of this setup

are:

1 Since the axis’ are horizontal, and no tilting

capability, we can only measure the horizontal

distance between camera to the wall in front of

UUV.

2 Vertical distance can be measured by tilting the

camera as well as the laser pointer. It means there

must be tilting actuators for the laser pointers. It is

not practical solution.

3 If camera and laser pointer axis’ are not parallel

(Fig. 7) we will have both horizontal and vertical

component. However, we cannot obtain linear

formula associates the two laser spots distance to

real world distance. It is proposed single laser

pointer mounting supports both horizontal and

vertical measurement. See Fig. 1 for the setup.

Distance measurement calculation A calibration wall or surface to obtain some

reference data had been formulated. All measurement

were scaled to and calculated based on those data.

The calibration process can be conducted in the air.

All data will be valid under water. Fig. 7 illustrates

the diagram of our design. Fig. 8 shows the associated

image captured by camera when laser beam hit the

calibration wall according to Fig. 7.

Figure 7 shows the projection of a laser spot at the

detected wall to the calibration wall. R is the distance

between camera’s center and the laser pointer’s center

as shown above. A’ is the distance between camera

lens and calibration wall. C is vertical distance

Fig. 5—Top View of UV Head with two laser pointers mounted

inside the dome

Fig. 6—Two laser pointers installation

Fig. 7—Geometric Analysis

INDIAN J. MAR. SCI., VOL. 38, NO. 3, SEPTEMBER 2009

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between camera center and laser spot at detected wall.

We will keep these values as reference data. What we

want to find is A, the horizontal distance between

camera and detected object. It is derived:

(B + R) / A = (B’ + R’) / A’

B/A + R/A = B’/A’ + R’/A’

R/A = R’/A’

A = A’ (R/R’) …(1)

This equation can be solved by referring to Fig. 8.

(R/R’) in equation (1) will be seen on the image as

(Ri/Ri’). So we can write:

(R/R’) = (Ri/Ri’)

Then equation (1) will be:

A = A’ (Ri/Ri’) …(2)

Where:

Ri = Ci – Bi’

Ri’= Rdi-Bi’

Ci and Rdi are measured directly based on image

processing. Bi’ is calculated by combining Figs 7 and

8 as follows:

B’/C = Bi’/Ci

Bi’= Ci (B’/C) …(3)

Where:

B’= A’ tan α

C = B’ + R

B’ and C can also be measured directly from the

calibration wall. Bi is a constant. This is a shift

distance caused by tilt angle of the laser pointer. If the

angle is zero, Bi’ is zero as well.

Finally, we calculate the vertical distance, D.

D = B + R

D = A tan α + R …(4)

Results and Discussion The testing purpose was to verify distance

measurement calculated by software with true

measurement. In this testing we only conducted the

horizontal measurement testing as depicted in Fig. 9.

First, we had to do a distance calibration. The purpose

of the calibration is to find the linear scale of pixel to

distance. We placed the camera 100 cm away from

the wall as shown in Fig. 10. With this setup, the

distance of the laser spot to the image center was

24 pixels. It meant that one pixel represents

24/100 cm. The measurement range of this testing

was between 25 cm and 280 cm. Fig. 11 presents the

Fig. 8—Image of a laser spots wall. Parameters are measured in

pixel and are associated with parameters in Figure 7

Fig. 9—Testing setup

Fig. 10—Calibration setup

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Fig. 11—True distance versus measured distance

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complete results of the measurement testing

comparing the true distance and measured distance.

The results are graphed in Fig. 12 and the error

between the true distance and the measured distance

is given in Fig. 13.

There were the following three issues in the testing:

1 The camera uses a wide angle lens. As the result,

the image captured by the camera will be

distorted. We didn’t apply algorithm to

undistorted the image in this testing.

2 Standard laser pointer cannot yield bright enough

spot for underwater camera to detect. Laser spot

on the wall more than 60 cm away from the

camera was hard to detect. It was our major

problem.

3 The color of laser spot detected by camera is not

only red, but also white. The center of a laser spot

is very bright, so it is detected by the camera as

white spot. Our previous algorithm is not aimed

for detecting a white spot. We had to use

additional algorithm to detect this white spot.

Conclusions

The present study has suggested a low cost but

powerful distance measurement system for UUV. The

design needs no refraction index data although it

works underwater. It gives us both horizontal and

Fig. 12—Graph of true distance versus measured distance

Fig. 13—Error between true distance and measured distance

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vertical measurement. Our future project would be

embedding this applications into a low cost single

board computer instead PC or DSP. It should be

possible since the calculation and its image processing

are not so complex, we expect that the proposed

system would work with comparable performance.

Based on issues and the testing results, it is concluded

that the effective range for the measurement system is

30 cm to 150 cm with maximum error of 10 cm. To

increase the measurement range it is planned to use a

stronger/brighter laser beam and enlarge the distance

between the camera center and the laser pointer

center. The image distortion did not affect the

accuracy of the measurement

Acknowledgement

The corresponding author was supported by the

MKE (Ministry of Knowledge Economy), Korea

under the ITRC (Information Technology Research

Centre) support program supervised by the IITA

(Institute for Information Technology Advancement)

(IITA-2009-C1090-0902-0026).

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