Vision based distance measurement system using single laser pointer design for underwater vehicle

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1 Indian Journal of Marine Sciences Vol. 38(3), September 2009, pp Vision based distance measurement system using single laser pointer design for underwater vehicle Muljowidodo K 1, 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 [ 2 Center for Unmanned System Studies (CentrUMS), InstitutTeknologi Bandung (ITB), Ganesha 10, Bandung, West Java, Indonesia [ 3 Corresponding Author, Department of Aerospace Information, Smart Robot Center, Konkuk University 1 Hwayang-Dong, Seoul , Korea [ 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 elsewhere 1. 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 *Author for Correspondence 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. Fig. 1 Underwater vehicle platform with camera and laser pointer

2 MULJOWIDODO et al.: VISION BASED DISTANCE MEASUREMENT SYSTEM 325 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 MSER 2, 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 elsewhere 4. 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 papers 5. 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 Flegh 5 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

3 326 INDIAN J. MAR. SCI., VOL. 38, NO. 3, SEPTEMBER 2009 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 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 Fig. 4 Camera mounting setup

4 MULJOWIDODO et al.: VISION BASED DISTANCE MEASUREMENT SYSTEM 327 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: 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 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 Fig. 6 Two laser pointers installation Fig. 5 Top View of UV Head with two laser pointers mounted inside the dome Fig. 7 Geometric Analysis

5 328 INDIAN J. MAR. SCI., VOL. 38, NO. 3, SEPTEMBER 2009 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 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. (B + R) / A = (B + R ) / A B/A + R/A = B /A + R /A R/A = R /A A = A (R/R ) (1) Finally, we calculate the vertical distance, D. D = B + R D = A tan α + R (4) 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 ) Where: Ri = Ci Bi Ri = Rdi-Bi (2) 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 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 Fig. 9 Testing setup Fig. 8 Image of a laser spots wall. Parameters are measured in pixel and are associated with parameters in Figure 7 Fig. 10 Calibration setup

6 MULJOWIDODO et al.: VISION BASED DISTANCE MEASUREMENT SYSTEM 329 Fig. 11 True distance versus measured distance

7 330 INDIAN J. MAR. SCI., VOL. 38, NO. 3, SEPTEMBER 2009 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

8 MULJOWIDODO et al.: VISION BASED DISTANCE MEASUREMENT SYSTEM 331 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-C ). References 1 Muljowidodo K, Sapto Adi N, Said D Jennie & Agus Budiyono Design, Development and Testing Underwater Vehicle: ITB Experience. In Proceeding of International Conference On Underwater System Technology: Theory and Applications, Penang Malaysia. 2 Lowe D G, Distinctive image features from scale-invariant key points. In International Journal of Computer Vision, Matas J, Chum O, Urban M & Pajdla T, Robust wide baseline stereo from maximally stable external regions. In Proc. of British Machine Vision Conference, pages , Rzhanov Y, Mamaenko A & Yoklavich M, UVSD: Software for Detection of Color Underwater Features. 5 Fleyeh H, Traffic and Road Sign Recognition. In thesis, Napier University, 2008.

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