Image processing in UAVObstrucle detection and path planning

Literature review

• Xiaomin Guo and Feihong Yu introduced a method of automatic cell counting based on microscopic images. Histogram information is used to calculate adjustable lower and upper threshold value.

• Venkatalakshmi. B et al. presented a method for automatic red blood cell counting using hough transform.

• Vision-based Autonomous Landing of an Unmanned Aerial Vehicle by Srikanth Saripalli, James F. Montgomery_ and Gaurav S. Sukhatme.

• Fitzgerald, Daniel L. and Walker, Rodney A. and Campbell, Duncan A. A Vision Based Emergency Forced Landing System for an Autonomous UAV. This paper introduces the forced landing problem for UAVs and presents the machine vision based approach taken for this research.

Literature review

• Multiple View Motion Estimation and Control for Landing an Unmanned Aerial Vehicle. The problem of using computer vision to estimate the motion of an unmanned aerial vehicle (UAV) relative to a landing target has recently been an active topic of research.

• Templeton et al. investigated the landing of aerial vehicle using model predictive control technique.

Literature review

Need for the current study• We must combine all the algorithms efficiently

so that we can detect and segregate the objects in the scene.

• We cannot have 10 or 20 sensors for a single UAV it will increase the complexity of the system and loss of power is more too.

Objectives

• Autonomous landing on elevated surfaces.

• Entering into buildings by analysing the available spaces.

Feasibility analysis

• Image processing in UAV in a part of the online video processing where we choose a random frame and apply a threshold, then we segment the image to obtain the objects from the image.

• We can program the UAV based on the objects location in the image.

Scope of the project

• Image processing based UAV is not completely operational as it is there is a manual intervention of a camera and joystick.

• It will reduce the man work time and complexity of the work .

Proposed methodology

• Image processing in UAV in a part of the online video processing where we choose a random frame and apply a threshold, then we segment the image to obtain the objects from the image.

• We can program the UAV based on the objects location in the image.

Time-line of activities• Literature reviews• Previous papers • Outcome of the previous projectsDec• Matlab image processing codes.• Algorithms and image processing techniquesJan• Image compression technique• Image segmentation techniqueFeb • Integration with camera and controller

Mar• Implementation in real-time system

Apr

Major references• Srikanth Saripalli, James F. Montgomery and Gaurav S. Sukhatme , “Vision-based

Autonomous Landing of an Unmanned Aerial Vehicle”, University of Southern California, Los Angeles, CA 90089-0781.

• Colin T Theodore, Mark B Tischler, “Precision Autonomous Landing Adaptive Control Experiment (PALACE)”, 25th Army Science Conference, Orlando, Fl, Nov 27-30, 2006.

• Andrew Miller and Mubarak Shah and Don Harper, “Landing a UAV on a Runway Using Image Registration”, University of Central Florida, 4000 Central Florida Blvd, Orlando FL, 32816.

• Todd Templeton, David Hyunchul Shim, Christopher Geyer, and S. Shankar Sastry, “ Autonomous Vision-based Landing and Terrain Mapping Using an MPC-controlled Unmanned Rotorcraft”.

• Accurate and Efficient Face Recognition from Video by Ognjen Arandjelovi´c Trinity College University of Cambridge.

• Achieving Illumination Invariance using Image Filters by Ognjen Arandjelovi´c, Roberto Cipolla Department of Engineering, University of Cambridge.

• Development of a vision-based ground target detection and tracking system for a small unmanned helicopter by LIN Feng, LUM Kai-Yew, CHEN Ben M.† & LEE Tong H.