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Optic Flow QuadCopter Control

Update Presentation 1 Weeks 1-4. Optic Flow QuadCopter Control. Oscar Merry. Contents. Introduction OpenCV Honeybee Vision Research Paper 1 – AR Drone LabVIEW Toolkit Research Paper 2 – Optical Flow Quadrotor controller Other Research Papers General Problems Aims of Project

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Optic Flow QuadCopter Control

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  1. Update Presentation 1 Weeks 1-4 Optic Flow QuadCopter Control Oscar Merry

  2. Contents • Introduction • OpenCV • Honeybee Vision • Research Paper 1 – AR Drone LabVIEW Toolkit • Research Paper 2 – Optical Flow Quadrotor controller • Other Research Papers • General Problems • Aims of Project • Discussion

  3. Introduction • Optical Flow: • The estimation of the motion field created by a moving camera with respect to a rigid scene. • Last 4 weeks: • Literature Analysis • Research into Optic Flow • Research into honeybee navigation • Research into OpenCV

  4. OpenCV • Open-source C / C++ library for advanced computer vision. • Built in functions for many optical flow processing techniques: • Canny edge detector • Feature tracking • Lucas–Kanade • Horn-Schunck • Shi-Tomasi

  5. Honeybee Vision • Each compound eye in the honeybee contains ~ 5000 ommatidia • Each ommatidium has 9 photoreceptor cells grouped into 3 classes • Ultraviolet sensitive • Blue sensitive • Green sensitive • Bees control their flight speed by keeping optical flow constant. (The same process is used for landing) This is done via the Green photoreceptors. • Bees recognise objects both through strong contrasts in luminance or colour and through optical flow.

  6. Research Paper 1 - AR Drone LabVIEW Toolkit • Michael Mogenson Masters Thesis: • “A software framework for the control of low-cost quadrotor aerial robots.” • Created a LabVIEW toolkit for the control of Parrot AR Drone.

  7. AR Drone LabVIEW Toolkit • Toolkit has multiple virtual instruments. (VIs) • Regular CommsVIs: • Main VI for control commands and comms management • Video VI for video reading and decoding • Nav Data VI for reading navigation data • ‘Thinking’ Vis: • State VI to estimate position in X,Y,Z Cartesian coordinates • Various image processing Vis (Fast blob detection, dense optical flow, image space conversion, ROI VI) • Toolkit has wrapper for OpenCV Library.

  8. AR Drone LabVIEW Toolkit – State VI • Populates a rotation matrix between the drones coordinate system and the ground using Euler orientation angles from navigation data. • Applies rotation matrix to the velocities measured from optical flow from the bottom camera. • Rotated velocities integrated into a position with the timestamp data. • Suffers from problem of drift.

  9. AR Drone LabVIEW Toolkit – Achievements • Benefit of LabVIEW – modifications without recompiling • Demonstrations: • Face tracking • Indoor hallway navigation (Via vanishing point) • Fly through hoop • Problems: • Indoor hallway navigation fails if 90 degree turn or if facing wall • State Estimation VI suffers from drift

  10. Research Paper 1 - Optical Flow Quadrotor controller • “Optical Flow-Based Controller for Reactive and Relative Navigationdedicated to a Four Rotor Rotorcraft” • Eduardo Rondon, Isabelle Fantoni-Coichot, Anand Sanchez, Guillaume Sanahuja • Produced a controller for a Quadrotor based on the optical flow from 2 cameras. (1 for velocity regulation, 1 for obstacle avoidance) • Implemented obstacle avoidance for indoor navigation.

  11. Optical Flow Quadrotor controller

  12. Optical Flow Quadrotor controller - Achievements • Velocity regulation via optical flow controller. • Sets the inverse time-to-contact to a threshold to stop the vehicle if obstacle detected. • Has lateral and altitude avoidance.

  13. Other Research Papers • “Optic flow based slope estimation for autonomous landing” de Croon et al. • Achieved slope following and autonomous landing. • “Combined Optic-Flow and Stereo-Based Navigation of UrbanCanyons for a UAV” Hrabar et al. • Used optical flow to balance UAV in canyon. • Used stero vision to navigate T and L junctions. • “An adaptive vision-based autopilot for mini flying machines guidance, navigation and control.” • used optic flow and IMU data for guidance navigation and control, specifically automatic hovering, landing, and target tracking. • Use feature tracking to reduce optic flow computation.

  14. General Problems • Camera Calibration – In order for the depth of a scene to be known the camera must be calibrated on a known object. (Or height above ground must be known) • Video Delay – Video encoding, decoding, and transmission must be fast enough.

  15. Project Aims • Hovering Stabilisation • Position and velocity control • Smooth Landing Execution • Obstacle recognition and avoidance (with a focus on methods that have similarities to honeybees e.g. flower recognition)

  16. Discussion • Communication: • Hardware • State commands (roll angle, pitch angle, yaw rate, climb rate, ??) • Video frames • Formalize state commands.

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