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Obstacle Detection for Low Flying UAS Using Monocular Camera

Obstacle Detection for Low Flying UAS Using Monocular Camera. Fan Zhang, Rafik Goubran , Paul Straznicky May 16, 2012. Introduction. Autonomous navigation for low flying UAS requires accurate terrain elevation map, which demands accurate range measurement. Methods:

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Obstacle Detection for Low Flying UAS Using Monocular Camera

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  1. Obstacle Detection for Low Flying UAS UsingMonocular Camera Fan Zhang, RafikGoubran, Paul Straznicky May 16, 2012

  2. Introduction • Autonomous navigation for low flying UAS requires accurate terrain elevation map, which demands accurate range measurement. • Methods: • Range measurement at a point: • Laser Rage Finder • Ultrasonic Signal • Range measurement for the entire field of view • 3D flash Lidar • Image sensor with computer vision algorithm Inertial Aided Inverse Depth Extended Kalman Filter (EKF)

  3. Inertial Aided Inverse Depth Extended Kalman Filter (EKF) • Sparse terrain model by features detection and tracking. • Less noisy and faster speed • Inertial Measurement • Better prediction of sensor location • Inverse Parameterization for Features • Features at near infinity • Early integration of features into the EKF • Camera Centric Coordinate • Improve linearity when camera travels away from the world frame origin • Extended Kalman Filter • Gradually fuses new information with existing measurement

  4. Camera Centric Inverse Parameterization

  5. Full State Vector • World frame coordinate and orientation in camera frame. • camera motion parameters : • feature coordinate in camera frame

  6. Algorithm Flow Diagram

  7. Measurement Model • Projection vector viewed at the predicted camera location • Projection onto image plane

  8. Data Collection GS-111M GPS Antenna

  9. Image Example

  10. Result and Discussion

  11. Range measurement over Iterations

  12. Elevation Map from Camera vs. Ground Truth Elevation Map Constructed from Features Parameters Ground Truth Digital Elevation Map (DEM)

  13. Elevation ErrorA Point to Point Comparison Mean Error = 15.87 meters Standard Deviation = 20.93 meters Max. Difference = 69.93 meters Min. Difference = 2.87 meters Max. Reported DEM Error = 16 meters

  14. UAV Position Estimated from Camera

  15. Conclusion • An inertial aided inverse depth Extended Kalman Filter framework • Outdoor flight video data collected using a SUAS towed by a helicopter • Result of the algorithm provides accurate estimates for features positions and the sensor’s own location • To obtain high resolution terrain elevation map, camera with high resolution and high dynamic range is required.

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