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Landing a UAV on a Runway Using Image Registration

Landing a UAV on a Runway Using Image Registration. Andrew Miller, Don Harper, Mubarak Shah University of Central Florida ICRA 2008. Overview. System for landing a UAV on a runway Small RC airplane Only sensor is a fixed, forward-looking camera Finds the runway using SIFT registration

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Landing a UAV on a Runway Using Image Registration

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  1. Landing a UAV on a Runway Using Image Registration Andrew Miller, Don Harper, Mubarak Shah University of Central Florida ICRA 2008

  2. Overview • System for landing a UAV on a runway • Small RC airplane • Only sensor is a fixed, forward-looking camera • Finds the runway using SIFT registration • Linear control system • Experiments • Microsoft Flight Simulator (no flight model) • Partial implementation on a real UAV

  3. Ground Station RC Plane with Camera Laptop Computer for Vision Processing and Control Algorithms Remote Control (72 Mhz) Analog Video (NTSC 900 Mhz) Human operator for high level control Block Diagram 30 frames per second 720x480 pixels RGB Downsample to 360x240

  4. Main Steps • Locate the runway in each video frame • Estimate the attitude of the UAV • Steer the UAV towards the runway maintaining the correct glideslope

  5. Vanishing point Base point 1. Locate the Runway • Base point and vanishing point (location and orientation)

  6. Test Frame Warped Reference Frame Planar Homography • The 3x3 planar homography matrix projects every point in the reference frame to the corresponding point in the incoming video frame Reference Frame

  7. Find the Homography usingSIFT and RANSAC • SIFT Feature Matching • 200-500 feature points, 100-200 matches • Chosen greedily, least ambiguous first • Planar homography between correspondences • RANSAC to discard outliers

  8. Stack of Reference Frames • Prepare a reference frames from a video • Annotate the runway and vanishing point • Sample the frames (more samples at lower altitudes) • Terrain features are important (not just the runway)

  9. Stack of Reference Frames

  10. Previous Closest Match SIFT Matching Using the Stack • Keep track of the current index • Highest number of SIFT matches = most similar viewpoint • Only need to compare adjacent frames

  11. 2. Estimate the UAV Attitude • 6 Degrees of Freedom • Pitch, Bank, Heading, Elevation, Distance, Course • Strategy • Ignore Distance • Find Pitch and Bank from the horizon line (x-axis) • Find Elevation, Heading, Course from the runway

  12. Too High On Target Too Far Right Intuitive Geometry • Relationship between runway appearance and UAV attitude • This is how human pilots land visually

  13. Formal Geometry • 3D Projection • C = Internal Calibration • R = External Calibration • Small Angle Approximation • Assume the UAV is flying smooth and level

  14. 2. Estimate the UAV Attitude • Recover the orientation parameters • Vanishing point of the runway • Beginning of the runway

  15. Correct Horizon Wrong Horizon Find the Horizon • Horizon estimation algorithm by Ettinger, et al. • Based on Differing Color Distributions • Used to recover two (pitch / bank)

  16. Course PI1 Elevation Heading PI1 PI2 Pitch Bank PI2 PI3 3. Control the UAV • Cascaded Linear Feedback Controller • Two separate chains • Two gains • Proportional • Integral • Intuitive • If UAV is too far right, steer left • If UAV is too high, pitch down • Bank angle is derivative of heading, heading is derivative of course • Pitch is derivative of elevation

  17. Autopilot GUI

  18. Algorithm Performance • Multiple stages • Control loops run at 50 Hz • Integrates smoothly even while input stays same • Horizon detection runs at 10 Hz • Pitch and bank are the most sensitive • Runway detection runs at 2 Hz • Elevation and course are the least sensitive

  19. Simulator Results • Microsoft Flight Simulator • Simulator-in-the-loop (separate computer) • ICRA08_1140_VI_fi.mp4 • Horizon 2007 09 Sep 11 Tue 08.13pm.avi

  20. Simulator Results • Error from earth curvature

  21. Actual UAV Experiments • Only using partial implementation • Horizon stabilization • Road following (no runway available) • Only brief periods of autonomous control

  22. Horizon Stabilization - Results

  23. Conclusions • Successful but imprecise landings • Performance is applicable to Cessna • Slower and more stable than actual UAVs • Assumption of linear system is not applicable near the runway • This is why the aircraft oscillates before landing • Future work • Incorporate flight model into controller design

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