Visual Obstacle detection in an UAV

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Transcript of Visual Obstacle detection in an UAV

  1. 1. Page | 1 Table of Contents 1 Introduction .............................................................................................................4 1.1 Motivation.........................................................................................................4 1.2 Visual Obstacle Avoidance Problem................................................................5 1.2.1 Definitions.................................................................................................5 1.2.2 Problem Statement...................................................................................6 2 Image Processing.....................................................................................................7 2.1 Understanding the Problem ..............................................................................7 2.2 Multi-View Relations .......................................................................................8 2.2.1 Two - view geometry................................................................................8 2.3 Epipolar Geometry............................................................................................8 Figure 2.1: Epipolar geometry of a scene from two images ......................................9 2.3.1 Properties of the Fundamental Matrix ..................................................9 2.3.2 Computation of the Fundamental Matrix ...........................................10 2.3.3 Random Sampling Consensus...............................................................10 2.3.4 Normalized 8 point algorithm...............................................................11 2.4 Homography ...................................................................................................12 2.4.1 Planar Homography ..............................................................................13 2.4.2 Homography between parallel planes of a scene.................................14 2.4.3 Image Warping.......................................................................................14 2.4.4 Computing the homography matrix.....................................................15 2.5 Edge Detection................................................................................................16 2.6 Corner Detection.............................................................................................17 2.7 Image Segmentation .......................................................................................18 2.7.1 K-means clustering ................................................................................19 3 DESIGN ................................................................................................................20 3.1 Requirements ..................................................................................................21 3.2 Design Methodology ......................................................................................22 3.2.1 System Design Model.............................................................................22 3.4 Hardware Constraints .....................................................................................23
  2. 2. Page | 2 3.5 Development Environment.............................................................................23 3.5.1 The MATLAB Environment.................................................................24 3.5.2 Differences between C and MATLAB .................................................24 3.6 Design Plan.....................................................................................................26 3.6.1 Descriptions of Sub-problems...............................................................27 4 IMPLEMENTATION ...........................................................................................29 4.1 Point Correspondences ...................................................................................29 4.1.1 Corner detection.....................................................................................29 4.2.2 Matching Corners ..................................................................................29 4.3 Homography Computation .............................................................................33 4.4 Warping Images..............................................................................................37 4.4.1 Warp Comparator .................................................................................40 4.5 Fundamental Matrix Computation..................................................................42 4.6 Image Segmentation and Edge Detection.......................................................42 4.6.1 Colour Segmentation and Edge Detection...........................................42 4.6.2 Object Segmentation..............................................................................43 4.8 Contour matching.........................................................................................47 5 Evaluation..............................................................................................................49 5.1 Point Correspondences ...................................................................................49 5.1.1 Corner detection.....................................................................................49 5.3.2 Matching correspondences....................................................................49 5.3 Computing homography and corner heights...................................................52 5.4 Computing epipolar geometry and contour heights........................................56 5.5 Image warping and obstacle detection............................................................57 5.6 Segmentation and obstacle detection..............................................................59 5.7 Summary of Evaluation ..................................................................................62 6 Appendix ...............................................................................................................63
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  4. 4. Page | 4 1 Introduction In this part, we will understand the motivation behind this work; the problem statement and need to find the solution to the problem will be discussed; overall objective & goal of the thesis will be described; its significance will be discussed; the methodology & approach adapted will be explained briefly; the details of contributions & publications will be cited; finally the format of the report will be presented to enlighten you on what is going to be discussed in further chapters. 1.1 Motivation All the aircrafts around the world, are currently using Traffic Alert & Collision Avoidance System (TCAS) to reduce the risk of mid-air collision. The logic used to avoid the collision has evolved over the course since 1970s when it was first brought into application. The Main component of TCAS is onboard beacon radar surveillance that monitors the local air traffic. The iterative process involves series of specific logics that are implemented in codes to evaluate the system through simulation using number of encounter models. In recent years, the interest in Unmanned Air Vehicles grown rapidly. UAV cover a wide range of applications from military, security & surveillance to commercial services. In last two decades, the application and industry for UAV utilization has grown tremendously. Industries around the world are understanding & becoming aware of the functionality and capabilities of an autonomous unmanned aerial vehicle that varies in shape, size, function & performance. For growth and development of UAV industry and its application to continue on fast scale, it is important for UAVs to operate freely and provide capable performance at equivalent level of safety as compared to that of manned aircraft. Hence, Collision avoidance is emerging as a key issue for UAV access to Civil Airspace.
  5. 5. Page | 5 1.2 Visual Obstacle Avoidance Problem 1.2.1 Definitions In the literature, we found that there are two different research approaches in the name of collision avoidance. In this thesis, we have used distinct definitions that enabled us to break the problem into various categories. The most important difference that is needed to be declared foremost is UAV avoiding collisions with stationary obstacles as opposed to air traffic. Hence, we define: Cooperative Collision Avoidance, where two aircrafts that are in communication with one another, negotiate a mitigation strategy. We are not concerned with Cooperative Collision Avoidance in this thesis. We are considering our obstacle to be static (like buildings or terrain) or flying (like hostile missiles). Non-Cooperative Collision Avoidance, where the aircraft is solely responsible to find a way to avoid the conflict scenario with the Terrain, Building or Intruder Aircraft. NFZ (No Fly Zones): Distinguished/ sensitive area declared by the Military for safety and security issues. Cannot be crossed, only avoid. Static in nature. E.g. Bhabha Atomic Research Center. In the thesis, we make use of standard definitions given by FAA or to be more specific defined by Minimum Operation Performance Standards. These standard, where ever required have been redefined to accommodate the context of our problem. Near Mid Air Collision (NMAC) when two aircrafts come within 500 feet horizontally, which is 152.3metres & 500 feet vertically. Safety Bubble the sphere with radius 500 feet around the UAV at any instant, within which any aircraft is an intruder, considered to be a potential conflict threat. Conflict Scenario an encounter scenario between two UAVs where the intruder comes within 500 feet. Collision Scenario an encounter where two UAVs collide with one another if avoidance fails. Mid Air Collision (MAC) when two UAVs collide during the course of flight.
  6. 6. Page | 6 1.2.2 Proble