Stereo Vision for Autonomous Vehicle Routing Using Raspberry PI

International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 2, February 2015)

Stereo Vision for Autonomous Vehicle Routing Using Raspberry PI Aditi R. Dakhane1, Manish. P. Tembhurkar2 1

M.E. Student, Embedded System and Computing, GHRCE, Nagpur, India Asst. Prof., Dept. of Computer Science and Engineering, GHRCE, Nagpur, India

2

Abstract- In the present scenario, stereovision deals with 3D images which calibrate the objects. The calibration procedure discovers the intrinsic and extrinsic parameters of stereo rig. Stereovision captors left and right images with or without any obstacle presence for calibration. Our objective is to develop an autonomous system which should be able to monitor the objects, obstacle, buoys etc. and it will trigger the navigation system and intimate the bus station about it. We propose to develop a system using small size computer circuit (Raspberry PI). We propose an approach of two frontal stereovision cameras (which are mounted on water navigation system) for Detection and avoidance of the object. Our system monitors the object on routing path and calibrate the alternate route. The final system should be capable of identifying and following the destination path in a distance of over 5 meters. Keyword- Camera, Raspberry Pi, Checkpoints

I. INTRODUCTION Fig.1. Stereo Rig Camera set up

Nowadays, computer vision is one of the most promising areas in which robotic field is quickly developed. Therefore need of water vehicle is important for marine obstacles. Generally, vehicles are being operated manually or by using image sensor which has been used by strong network monitoring. These kind of vehicles are more complex and usually used for highly complex missions. There cost is also high. In recently ARM processors are extensively used in consumer electronics devices such as mobile phones, pocket calculators, multimedia players and PDAs (personal digital assistants). Such as the Raspberry PI or the even more powerful Quad-Core ODROID-U2, devices under USD 90, allowing the off-the-shelf robotics era to begin [1]. Raspberry PI is small kit means it is a small computer. This small computer performs a number of tasks. Systems like the one described in use computer vision to detect the horizon line and specific objects in the scene as an aid to a small sailboat guidance. Some applications have also been developed being capable of avoiding obstacles in specific water and scenery conditions using stereovision [3].

Shape and Color are two things which are greater cues for identifying objects of interest. By using monocular vision and very simple algorithms, one can easily infer the orientation of a certain point with relation to the camera reference system. On the other hand, a single camera doesn’t supply us with the depth information on that same point. This happens because all the points on the same depth ray will be represented by a single point on the camera image plane [1]. Here we are using more than one camera for solving the problem. Then the next camera means the second camera captures the same distance from the other view which will have the same point represented in a different location of its image plane and calibrate the images. For each camera calibration, there are two parameters for the stereo rig that are intrinsic parameter and extrinsic parameters. These two parameters are performed different tasks means in intrinsic parameters contain the geometrical and optical specificities of the camera in which lens distortion coefficient, focal distance, principal point, etc. while in the extrinsic parameter there are the ones that contain related the unknown reference frame of the camera to know one. Here this is the simplest way of running this procedure is used each camera to collect the images of a chess board which is known dimensions as from different perspectives.

II. STEREO V ISION Stereo Vision based on 3D concept in which two cameras are fixed in parallel distance and capture images of infinite distances will appear on the same corresponding pixel.

98

International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 2, February 2015) Problem statement      

Generally an object detection performed manually. The system captures the 2D image for image processing. While leading to inaccurate result. Can not calculate the distance. Size of the object. Complex process to calculate the route manually with respect to two reference points. Fig. 1. : Block Diagram of the System

III. LITERATURE SURVEY

The measured distance is calculated on the basis of travel time. The formula to calculate the distance is shown below:

There are many more distance calculated techniques as per the technologies. LASER is the system which has also calculated distance, but it will not calculate long distance areas. Also, there are ultrasonic sensors for calculating distances which are based on sound. But in this project we are using Raspberry PI. It is the small computer which is handling lots of tasks. Next important hardware for this is stereo vision cameras which are fixed in the ocean boat and continuously taking images which is calculating distances and if there is any obstacle in between the object the boat will automatically change its path. The Raspberry PI is powered by continuous power supply through battery generator and having two computational units for the continued communication. Linux is the operating system. Author [1], Investigated and an approach by using single laser pointers. Calculating horizontal and vertical distance between an object and camera. Under the water distance, calculate by using LASER. Calculating horizontal and vertical distance between an object and camera. It's very difficult to detect 60 cm distance obstacle. It will not detect white spot only the red spot. Author [2], Ultrasonic sensors are used for distance calculation to the obstacle or object. Ultrasonic sensors are very versatile in distance measurement. They are also providing the cheapest solutions. Ultrasound waves are useful for both the air and underwater. Here we are calculating the distance of sewer blockages for underground pipelines. In the sewer inspection system under development and testing this system is mounted on the front portion on an automatic robotic vehicle which will move inside the fully or semi-filled sewer pipeline. The system will compute the distance of obstacles or blockage store it and also communicate the distance or location of the obstacle or blockage to the control station above ground.

Distance (cm) = (Travel Time*10-6 * 34300) / 2 The ultrasonic waves travelled to and from the object, hence the whole distance is divided by two. Hence the system is difficult for measuring shortest distance. Author [3], the region of interest (ROI) in disparity mapping for stereo vision, autonomous guided vehicle (AGV) using block matching algorithm. Disparity mapping means the corresponding two images representing the same point of the scene. The set of displacements between matched pixels is usually indicated as disparity map. Here the sum of the absolute distance algorithm is used for distance calculation. IV. SYSTEM ARCHITECTURE This Block diagram follows the steps for the vehicle which calibrate the capturing images.

Fig. 3. Block Diagram For Stereovision Vehicle

V. PROPOSED METHODOLOGY In this proposed framework capturing the images and find the shortest path.

99

International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 2, February 2015) [5]

Marcus Svedman Luis Goncalves, Niklas Karlsson, Mario Munich, Paolo Pirjanian, “Structure from Stereo Vision using Unsynchronized Cameras for Simultaneous Localization and Mapping”, IEEE/RSJ International Conference on Intelligent Robots and Systems 2005. [6] Koichiro DEGUCHI, “A Direct Interpretation of Dynamic Images and Camera Motion for Vision Guided Robotics”, IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems 1996. [7] Didier Gemmerli, Alain Filbois and Houda Chabbi,” Construction of 3d views from stereoscopic triplets of images”, IEEE 1994. [8] L. P. Perera , L. Moreira, F. P. Santos, V. Ferrari, S. Sutulo and C. Guedes Soares ”A Navigation and Control Platform for RealTime Manoeuvring of Autonomous Ship Models” 9th IFAC Conference on Manoeuvring and Control of Marine Craft (MCMC2012), Arenzano, Italy, September, 2012. [9] Jeemoni Kalita and Karen Das “Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean Distance Based Decision Making Technique” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No. 2, 2013. [10] Lurong Xiao and Edward Hung “An Efficient Distance Calculation Method for Uncertain Objects” Review Paper. [11] Anwar Hasni Abu Hasan, Rostam Affendi Hamzah, and Mohd Haffiz Johar“Region of Interest in Disparity Mapping for Navigation of Stereo Vision Autonomous Guided Vehicle” International Journal of Computer and Electrical Engineering, Vol. 2, No. 2, April, 2010.

In which routers are fixed and check the obstacle. If there is any obstacle, then the vehicle change their path and go to the next router. For that it will cover the destination. VI. CONCLUSION In this review paper, we check the routers and find the shortest path. Here this vehicle is for security and military applications. REFERENCES [1] [2] [3]

[4]

Ricardo Neves, Anibal C. Matos ,” Raspberry PI Based Stereo Vision For Small Size ASVs”, IEEE, 2013. Elisabet del Valle “Process and system for calculating distances between wireless nodes” , IEEE, 2011. Muljowidodo K, mochammad A Rasyid, SaptpAdi N & Agus Budiyono,”Vision based distance measurement system using single LASER pointer design for underwater vehicle ” ,Indian Journal of Marine Science, 2009. Saumitra Krishna, Sachin Kansal, Abhijit Makhal, Dr. Pavan Chakraborty, Prof. G.C. Nandi,” Systematic study of binocular depth finding using two web cameras”, Third International Conference on Computer and Communication Technology 2012.

100