obstacle detection for mobile robot - .obstacle detection for mobile robot pendyala kavya 111cs0445
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Department of Computer Science andEngineering,National Institute of Technology Rourkela,Rourkela- 769008, Odisha, India
the department of
Computer Science and Engineering
National Institute of Technology, Rourkela
in partial fulfillment of the requirements
for the degree of
Bachelor in Technology
by Pendyala Kavya
(Roll no. 111CS0445)
Under the supervision of,Dr. Ratnakar Dash
Department of Computer Science and Engineering,National Institute of Technology Rourkela,
Rourkela- 769008, Odisha, India
Dr Ratnakar DashAssistant Professor
This is to certify that the work in the project entitledObstacle detection for a mobile robot by Pendyala Kavyabearing roll number 111CS0445 is a record of her workcarried out under my supervision and guidance in par-tial fulfillment of the requirements for the award of thedegree of Bachelor of Technology in Computer Scienceand Engineering.
Dr. Ratnakar Dash,Assistant Professor,
Department of Computer Science and Engineering,NIT Rourkela, Rourkela , Odisha.
I hereby declare that all the work contained in this reportis my own work unless otherwise acknowledged. Also, allof my work has not been previously submitted for anyacademic degree. All sources of quoted information havebeen acknowledged by means of appropriate references.
I would like to thank my supervisor Professor RatnakarDash for his exemplary guidance, monitoring and for pro-viding me with an open and free environment to learnthings and implement them.I convey my regards to all the faculty members of De-partment of Computer Science and Engineering, NITRourkela for their valuable guidance throughout my jour-ney in NIT Rourkela. I would like to thank my friendsfor helping me out in times of necessity and being therefor me always.I would like to express my profound gratitude to my par-ents and sister for their support and blessings withoutwhich this task would not have been easier.
This project Obstacle detection and avoidance by a mo-bile robot deals with detection and avoidance of obstaclesof a mobile robot. Webcam captures images of the en-vironment in which the robot moves. Image processingmethods are then performed to identify the existence ofobstacles within the environment. Algorithms are im-plemented in MATLAB with Image Processing toolbox.Planar geometry and corner detection methods are usedin this obstacle detection method.Digital camera takesthe pairs of images of the scene. Using planar homogra-phy warped image is formed. Obstacle detection is doneby comparing the warped image with the final image.
Keywords: Obstacle Detection, Mobile Robot, MAT-LAB.
1 Introduction 21.1 Obstacle Detection . . . . . . . . . . . . . 21.2 Overview of report structure . . . . . . . . 3
2 Literature Review 52.1 Computer Vision . . . . . . . . . . . . . . 52.2 Corner Detection . . . . . . . . . . . . . . 52.3 Planar Homography . . . . . . . . . . . . 6
2.3.1 Homography relationship of two dif-ferent images of a plane . . . . . . 7
2.4 Computation of Homography matrix . . . 72.5 Image Warping . . . . . . . . . . . . . . . 112.6 Summary of Review . . . . . . . . . . . . 12
3 Implementation 143.1 Design plan . . . . . . . . . . . . . . . . . 14
3.1.1 Design Methodology . . . . . . . . 153.1.2 Hardware Requirement . . . . . . . 153.1.3 Software used . . . . . . . . . . . . 15
3.2 Description of sub problems . . . . . . . . 153.3 Summary of implementation . . . . . . . . 17
4 Evaluation 194.1 The Test Plan . . . . . . . . . . . . . . . . 194.2 Results of corner detection . . . . . . . . . 204.3 Result of matching corners . . . . . . . . . 214.4 Warped Image . . . . . . . . . . . . . . . . 224.5 Difference between warped and final image 234.6 Obstacle Detection . . . . . . . . . . . . . 24
4.7 Summary of this chapter . . . . . . . . . . 25
5 Conclusion 275.1 Overall Summary . . . . . . . . . . . . . . 27
List of figures
Figure 1 : Corners detected.............................................................21
Figure 2 : Matching corners on initial image....................................22
Figure 3 : Matching Corners on Final Image....................................22
Figure 4 : Warped image.................................................................23
Figure 5 : Difference between initial and final image........................32
Figure 6 : Initial image...................................................................24
Figure 6 : Obstacle detection..........................................................24
Image processing is processing of images, for example,photos or feature outlines. The yield is the changedadaptation of the data image or an arrangement of at-tributes or parameters identified with the image. Thecomputer evolution that has occurred throughout themost recent 20 years has given chances in the advance-ments in the field of advanced image processing. Thishas thus, opened up a huge number of applications indifferent fields, which could use technology.
1.1 Obstacle Detection
Obstacle detection is defined as The determination ofwhether a given space is free of obstacles for safe travelby an autonomous vehicle by Singh . It is reallyvery important for performing many other operations inmobile robots like navigation and avoidance.A good obstacle detection system must be capable of thefollowing [Singh]:
To detect obstacles on a given space in good time
To detect and identify correct obstacles
To identify and ignore ground features that may ap-pear as obstacles
Images of the real environment of the mobile robot aretaken using a webcam and these images are processed inthe computer that performs obstacle detection.
1.2 Overview of report structure
This thesis has five chapters:Chapter 1 titled INTRODUCTION introduces theproject. It gives us the objective of the project.
Chapter 2 titled LITERATURE REVIEW does allthe background study necessary for the implementationof the project. It includes basics of Image Processing andComputer Vision, their brief history, their applicationsand how they are used in the project to achieve the ob-jective of it.
Chapter 3 entitled IMPLEMENTATION it describesthe sub-problems of the main problem i.e., obstacle de-tection, provides proper solution to each sub-problem.Implements the solution of the sub-problems and com-bines the result to give the whole result of obstacle de-tection.
Chapter 4 entitled EVALUATION explains the testplan initially. conducts wide range of tests. Best of theresults are shown in this report.
Chapter 5 entitled CONCLUSION gives the sum-mary of the other chapters and concludes the report.
2 Literature Review
This chapter does all the back-ground study necessary togain enough knowledge of topics like Image ProcessingComputer Vision to implement this project. Sub prob-lems also like Corner Detection,Matching the corners,Computing Homography using RANSAC algorithm arealso studied here
2.1 Computer Vision
Computer vision is one of the sub fields of artificial intel-ligence in the field of computer science. Computer Visionis just like machine imitating human vision. Since bothforms of visions (Human vision and Computer Vision)are dependent on light radiated from the environment,Computer Scientists do no consider this to be an accu-rate one.
2.2 Corner Detection
Corners are distinguished by the huge variations of in-tensities in x and y directions. Corners detected whileanalyzing the images are used in various applications.Point correspondences are necessary to compute the ho-mography of a scene. Corners are normally chosen sincethey are the points which are easily distinguished andwill be easy to match them on other images.
Corner detection is done using intensity function I(x,y)of the pixels. The most used corner detection method isHarris corner detection methods. Here, in our project
the corner detection algorithm written by KLT and Har-ris is used. In this method Local structure matrix ofevery pixel is found out. With this matrix of every pixel,it is found out whether the pixel is corner or not.
Local structure matrix(A) of a pixel(x,y) with inten-sity function I(x,y) is
)In the event that there is noise in the image, it is smoothedby utilizing a gaussian channel w(r,s) with a preset s anda box filter before computing the local structure matrixof each pixel. Since A is symmetric, It has precisely 2positive eigen valuesThis is the essential standard which is utilized as a part ofHarris corner indicator  when we work with greyscaleimages. Algorithm for Harris Corner Detection method
For each pixel (x, y) find A
For each A find 2 eigenvalues max, min
Sort all min, discard pixel with small min.
Discard pixels with large max - min
Remaining are corner points
2.3 Planar Homography
Planar homography is described as the relationship be-tween corresponding points between two images of th