Navigation Functionalities for an Autonomous UAV Helicopter

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66 CHAPTER 5. SENSOR FUSION FOR VISION BASED LANDING noisy. Early attempts have been made in applying a low-pass filter to this velocity to remove the noise but the increased delay made the tuning of the control system more difficult. The velocity data provided by the Kalman filter has low latency as can be observed from the comparison with the GPS velocity. This is due to the fact that the Kalman filter takes advantage of the high frequency and low latency information from the accelerometers. The use of low latency velocity information allows for stable control during the landing approach. Figure 5.5: Comparison between velocity derived from raw vision position, sensor fusion and GPS for the North component. Figures 5.8 and 5.9, show a comparison between the attitude angles provided by the vision system alone (see Paper III for details as to how the vision system calculates the attitude angles), the attitude angles calculated
5.2. EXPERIMENTAL RESULTS 67 Figure 5.6: Comparison between velocity derived from raw vision position, sensor fusion and GPS for the East component.
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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
Page 25 and 26: 3.2. HARDWARE-IN-THE-LOOP SIMULATIO
Page 27 and 28: 3.3. REFERENCE FRAMES 17 3.3 Refere
Page 29 and 30: 3.4. THE AUGMENTED RMAX DYNAMIC MOD
Page 31 and 32: 3.4. THE AUGMENTED RMAX DYNAMIC MOD
Page 33 and 34: 3.5. SIMULATION RESULTS 23 3.5 Simu
Page 35 and 36: 3.5. SIMULATION RESULTS 25 Figure 3
Page 37 and 38: 3.5. SIMULATION RESULTS 27 Figure 3
Page 39 and 40: Chapter 4 Path Following Control Mo
Page 41 and 42: 4.2. TRAJECTORY GENERATOR 31 Figure
Page 43 and 44: 4.2. TRAJECTORY GENERATOR 33 Fig. 4
Page 45 and 46: 4.2. TRAJECTORY GENERATOR 35 be use
Page 47 and 48: 4.2. TRAJECTORY GENERATOR 37 4.2.3
Page 49 and 50: 4.2. TRAJECTORY GENERATOR 39 radius
Page 51 and 52: 4.2. TRAJECTORY GENERATOR 41 Calcul
Page 55 and 56: 4.2. TRAJECTORY GENERATOR 45 by the
Page 59 and 60: 4.3. OUTER LOOP CONTROL EQUATIONS 4
Page 61 and 62: 4.4. EXPERIMENTAL RESULTS 51 Up [m]
Page 63 and 64: 4.5. CONCLUSIONS 53 1 + s 1 C(s) =
Page 65 and 66: 4.5. CONCLUSIONS 55 Vel [m/s] 20 18
Page 67 and 68: Chapter 5 Sensor fusion for vision
Page 69 and 70: composed of three accelerometers an
Page 71 and 72: 5.1. FILTER ARCHITECTURE 61 filter
Page 73 and 74: 5.1. FILTER ARCHITECTURE 63 north,
Page 75: 5.2. EXPERIMENTAL RESULTS 65 Figure
Page 79 and 80: 5.2. EXPERIMENTAL RESULTS 69 by the
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
Page 107 and 108: A.3. PAPER III 97 A.3 Paper III Aut
Page 109 and 110: A.3. PAPER III 99 the projection of
Page 111 and 112: A.3. PAPER III 101 yes Tbo > Tlim2
Page 113 and 114: A.3. PAPER III 103 immediately afte
Page 115 and 116: A.3. PAPER III 105 altitude [m] 20
Page 117: A.3. PAPER III 107 Fig. 10. Flight
Page 121 and 122: Department of Computer and Informat
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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