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Chayatat Ratanasawanya March 16, 2011

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Overview Thesis problem The UAV Pose estimation by POSIT Previous work Development of POSIT-based real-time pose estimation algorithm Experimental results Questions 2

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Thesis problem statement Develop a flexible human/machine control system to hover an UAV carrying a VDO camera beside an object of interest such as a window for surveillance purposes. Method: Human control – Joystick Machine control – Visual-servoing Application: for the police to use the system to survey a room from outside of a building. 3

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The UAV Q-ball: 6DOF quadrotor helicopter Came with SIMULINK-based real-time controllers y x z World frame 4 HelicopterController X, Z (desired) Optitrack X, Z IMU Roll, Pitch Sonar Y Y (desired)Yaw(desired) Yaw Magnetometer Desired inputs X, Y, Z, Yaw Camera

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POSIT algorithm Developers: Daniel DeMenthon & Philip David The algorithm determines the pose of an object relative to the camera from a set of 2D image points 5 Reference: POSIT Image coordinates of min. 4 non-coplanar feature points 3D object coordinates of the same points Camera intrinsic parameters (f, cc) Rotation matrix of object wrt. camera Translation of object wrt. camera

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Previous work Cardboard box target Took still images of the target from various locations in the lab Manual feature points identification Object pose was estimated offline Target was self-occluded Not a real-time process 6 y x z Object frame

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Current work Image-based control algorithm is being developed Must be a real-time process UAV pose must be estimated real-time Target must not be self-occluded Image source: Live video Image processing has to be fast Feature points must be identified automatically 7

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Feature points extraction 8 Camera Detect LED Detect Window Detect Corners Discard unwanted feature points detected

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Distortion coeff. from cam calibration Feature points undistortion? Fast image processing – no unnecessary calculations Evaluate the pose estimated by POSIT from distorted and undistorted feature points locations 9 VDO from Camera Feature points extraction Undistortion by look-up table POSIT & Inv. kinematics Points location filter Compare Optitrack IMU POSIT & Inv. kinematics 6DOF UAV pose estimates 6DOF UAV pose Roll Pitch

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Experimental setup 10

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Experimental setup The Q-ball was randomly placed in 20 locations in the lab. Its pose was different in each location. Acquire live video stream and estimate the UAV pose with POSIT in real-time DOF pose estimations, Optitrack, and IMU readings were recorded. Optitrack readings are used as reference. 11

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Results - X 12 Test Undistorted points Distorted points Optitrack Standard Deviation

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Results - Y 13 Test Undistorted points Distorted points Optitrack Standard Deviation

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Results - Z 14 Standard Deviation Test Undistorted points Distorted points Optitrack

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Results - Roll 15 Standard Deviation Test Undistorted points Distorted points OptitrackIMU

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Results - Pitch 16 Standard Deviation Test Undistorted points Distorted points OptitrackIMU

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Results - Yaw 17 Standard Deviation Test Undistorted points Distorted points Optitrack

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Mean and SD of error of all 3000 measurements 18 DOF Distorted feature points w.r.t. to Optitrack Undistorted feature points w.r.t. to Optitrack Optitrack w.r.t. IMU MeanSDMeanSDMeanSD X (cm) N/A Y (cm) N/A Z (cm) N/A Roll () Pitch () Yaw () N/A DOF Distorted feature points w.r.t. to Optitrack Undistorted feature points w.r.t. to Optitrack Optitrack w.r.t. IMU MeanSDMeanSDMeanSD X (cm) N/A Y (cm) N/A Z (cm) N/A Roll () Pitch () Yaw () N/A Excludes #3 & 15

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Conclusion POSIT algorithm is an alternative for real-time UAV pose estimation Target consists of a white LED and a window 5 non-coplanar feature pts: the LED and 4 corners Pose estimation using undistorted feature points is more accurate than that using distorted points – significant improvement along Z-direction Image information may be mapped to positional control inputs via POSIT algorithm 19

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Summary Thesis problem & the UAV Previous work on POSIT – the drawbacks POSIT-based real-time pose estimation algorithm Feature points extraction from live VDO Feature points image coordinates undistortion Feature points location filtering Real-time algorithm Comparison between pose estimated by POSIT, pose from Optitrack, and 2 attitude angles from IMU. 20

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Homogeneous transformation is a matrix which shows how one coordinate frame is related to another. It is used to convert the location of a point between two frames. Homogeneous transformation y x z Frame C yx z Frame A (d x, d y, d z )

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The process of deriving the transformation (rotation and translation) between two frames from a known transformation matrix Inverse kinematics Translation Inverse kinematics formulas Rotation angles

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Inverse kinematics formulas ψ y x z θ

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Result calculation y x z World frame, W y x z Object frame, L y x z Q-ball frame, Q zx y Cam frame, C CTLCTL POSIT y x z Object frame, L

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Result calculation QTCQTC WTLWTL Translation Inverse kinematics formula Rotation angles y x z Object frame, L y x z Q-ball frame, Q zx y Cam frame, C CTLCTL y x z World frame, W

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