Navigation Functionalities for an Autonomous UAV Helicopter

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100 APPENDIX A. Fig. 3. Dataflow in the vision system. and circle radii. The estimation is optimized by minimizing the reprojection error: min a 12� � di − ci mod 4 i=1 σi mod 4 �2 d = (ue1, ve1, la1, lb1, · · · , ue3, ve3, la3, lb3) σ = �(σc, σc, σl, σl) c = ûe(a), ˆve(a), ˆla(a), ˆ � (3) lb(a) This function is non-linear and minimized iteratively using the fast-converging Levenberg-Marquardt method [6]. It’s initialized with the pose parameters from the first estimate. The uncertainties of the ellipse centers σc and axes σl are known from separate noise measurements. Finally, the pose parameters are converted to helicopter position and attitude using angles from the PTU and known frame offsets and rotations. The PTU control runs in parallel to the image processing using pixel coordinates of the pattern center as input, aiming at centering the landing pad in the frame as soon as it’s localized. Two methods for analyzing image intensities are implemented. The first estimates the background intensity of the reference pattern based on the assumption being the brightest surface in the image. When the landing pad is detected, the second method is applied. It computes the background inten-
A.3. PAPER III 101 yes Tbo > Tlim2 Tbo=0 no KF update filter initialization Tbo Fig. 4. The filter architecture. no ∆ P< ∆ max yes no INS mechanization Tbo > Tlim1 yes yes KF prediction no new camera update sity and the binarization threshold based on the intensity distribution of the pattern. The exposure controller controls the camera shutter time and iris aiming at keeping the background intensity in a certain range. 3 Sensor Fusion The position and attitude estimates provided by the vision system can not be fed directly into the controller due to their intrinsic lack of robustness: the field of view can be temporarily occluded (for example by the landing gear), the illumination conditions can change dramatically just by moving few meters (sun reflections, shades, etc.). On the other hand, vision readings are very accurate, when available. Hence, a navigation filter based on a Kalman filter (KF) has been developed, fusing highly accurate 3D position estimates from the vision system with inertial data provided by the on-board accelerometers and angular rate gyros. Besides filtering out a large part of the noise and outliers, the filters provides a satisfying dead reckoning capability, sufficient to complete the landing even when the vision system is ”blind” 1 , see Fig. 8. The implementation of the KF is done using the error state space or indirect formulation with feedback mechanization (Fig. 4). The states of the filter are the estimated inertial navigation system (INS) errors. The three observations are given by the difference between the INS position and the position from the vision system (lateral, longitudinal and vertical position relative to the pattern). The advantage of the indirect formulation versus the direct formulation (position, velocity and attitude are among the state 1 During the last 50 cm before touch down the vision system is often ”blind” due to two factors: (a) the shade of the helicopter covers part of the pattern at touch down, and (b) when the distance of the camera to the pattern is very small it is very hard for the controller of the pan/tilt unit to keep the pattern in the picture.
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Linköping Studies in Science and T
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Acknowledgements This work would ha
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Contents 1 Introduction 1 2 Overvie
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Chapter 1 Introduction An Unmanned
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of the environment in order to avoi
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Chapter 2 Overview This chapter pre
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2.1. UAV SOFTWARE ARCHITECTURE 7 wh
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2.2. THE UAV HELICOPTER PLATFORM 9
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2.2. THE UAV HELICOPTER PLATFORM 11
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Chapter 3 Simulation 3.1 Introducti
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3.2. HARDWARE-IN-THE-LOOP SIMULATIO
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3.3. REFERENCE FRAMES 17 3.3 Refere
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3.4. THE AUGMENTED RMAX DYNAMIC MOD
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3.4. THE AUGMENTED RMAX DYNAMIC MOD
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3.5. SIMULATION RESULTS 23 3.5 Simu
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3.5. SIMULATION RESULTS 25 Figure 3
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3.5. SIMULATION RESULTS 27 Figure 3
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Chapter 4 Path Following Control Mo
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4.2. TRAJECTORY GENERATOR 31 Figure
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4.2. TRAJECTORY GENERATOR 33 Fig. 4
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4.2. TRAJECTORY GENERATOR 35 be use
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4.2. TRAJECTORY GENERATOR 37 4.2.3
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4.2. TRAJECTORY GENERATOR 39 radius
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4.2. TRAJECTORY GENERATOR 41 Calcul
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4.2. TRAJECTORY GENERATOR 45 by the
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Page 59 and 60: 4.3. OUTER LOOP CONTROL EQUATIONS 4
Page 61 and 62: 4.4. EXPERIMENTAL RESULTS 51 Up [m]
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Page 71 and 72: 5.1. FILTER ARCHITECTURE 61 filter
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Page 75 and 76: 5.2. EXPERIMENTAL RESULTS 65 Figure
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Page 81 and 82: 5.3. CONCLUSION 71 vision system st
Page 83 and 84: BIBLIOGRAPHY 73 [9] E. Frazzoli. Ro
Page 85 and 86: Appendix A 75
Page 87 and 88: A.1. PAPER I 77 is a three-dimensio
Page 89 and 90: A.1. PAPER I 79 The reference point
Page 91 and 92: A.1. PAPER I 81 Path Av Err Max Err
Page 93 and 94: A.2. PAPER II 83 From Motion Planni
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Page 115 and 116: A.3. PAPER III 105 altitude [m] 20
Page 117: A.3. PAPER III 107 Fig. 10. Flight
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Page 123 and 124: No 598 Rego Granlund: C 3 Fire - A
Page 125 and 126: No 1024 Aleksandra Tešanovic: Towa
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Navigation Functionalities for an Autonomous UAV Helicopter

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